Data processing method, device, equipment and medium

文档序号：1888060 发布日期：2021-11-26 浏览：26次中文

阅读说明：本技术 数据处理方法、装置、设备及介质 (Data processing method, device, equipment and medium ) 是由蔡思佳童潘榕谢贤海陈均炫邓兵于 2020-05-22 设计创作，主要内容包括：本发明公开了一种数据处理方法、装置、设备及介质。其中,一种数据处理方法包括：获取道路数据和移动对象的图像数据；基于图像数据,获取移动对象的图像特征；基于图像特征和道路数据,确定移动对象的移动特征；对移动特征进行时空点位匹配,得到移动对象的行驶轨迹。根据本发明实施例,能够提高所还原的车辆的行驶路径轨迹的全面性和可靠性。(The invention discloses a data processing method, a data processing device, data processing equipment and a data processing medium. The data processing method comprises the following steps: acquiring road data and image data of a moving object; acquiring image characteristics of a moving object based on the image data; determining a movement feature of the moving object based on the image feature and the road data; and performing space-time point location matching on the moving characteristics to obtain the driving track of the moving object. According to the embodiment of the invention, the comprehensiveness and the reliability of the restored running path track of the vehicle can be improved.)

1. A method of data processing, comprising:

acquiring road data and image data of a moving object;

acquiring image characteristics of a moving object based on the image data;

determining a movement feature of the moving object based on the image feature and the road data;

and performing space-time point location matching on the moving characteristics to obtain the driving track of the moving object.

2. The method of claim 1, wherein the image data comprises a plurality of object images, each of the object images comprising a moving object;

wherein the obtaining image features of the moving object based on the image data comprises:

and performing image analysis on each object image to obtain image characteristics corresponding to the object images.

3. The method of claim 2, wherein the performing image analysis on the object image to obtain image features corresponding to the object image comprises:

acquiring the space-time characteristics of the object image; wherein the spatiotemporal features include an acquisition time of the object image and an acquisition location of the object image;

carrying out image recognition on the object image to obtain object characteristics of a moving object in the object image;

and taking the object feature and the space-time feature as image features corresponding to the object image.

4. The method of claim 2, wherein said acquiring image data of a moving object comprises:

acquiring video data, wherein the video data comprises a plurality of videos related to a moving object, and each video is acquired by image acquisition equipment;

and for each video, carrying out image interception on the video to obtain a plurality of object images.

5. The method of claim 4, wherein after the image capturing the video to obtain a plurality of object images, the method further comprises:

and carrying out duplication elimination processing on the plurality of object images to obtain the duplicated object images.

6. The method of claim 2, wherein the determining movement characteristics of the moving object based on the image characteristics and the road data comprises:

constructing a graph structure corresponding to each object image by using image features corresponding to a plurality of object images and the road data;

clustering graph structures corresponding to the object images based on a preset graph neural network to obtain at least one graph structure group; wherein each group of the graph structures is used as a moving feature of a moving object.

7. The method according to claim 6, wherein the constructing a graph structure corresponding to each object image by using the image features corresponding to the plurality of object images and the road data comprises:

establishing a road directed graph of the urban road according to the road data;

for each object image, matching the object image with the road network directed graph according to the space-time characteristics of the object image to obtain road network characteristics corresponding to the object image; wherein the spatiotemporal features include an acquisition time of the object image and an acquisition location of the object image;

determining the association characteristics between every two object images according to the image characteristics and the road network characteristics;

and constructing a graph structure corresponding to each object image according to the image features and the associated features.

8. The method of claim 7, wherein before the building of the road directed graph of the urban road according to the road data, the method further comprises:

acquiring network traffic data and GPS data; wherein the GPS data comprises at least one of image acquisition equipment GPS information and moving object GPS information on the urban road;

wherein, according to the road data, establishing a road directed graph of the urban road comprises:

and establishing the road directed graph according to the road data, the network traffic data and the GPS data.

9. The method of claim 7, wherein the moving object comprises a moving vehicle, the object image comprises a vehicle image, and the object features comprise at least one of:

license plate number characteristics, vehicle attribute characteristics, and vehicle appearance characteristics.

10. The method of claim 9, wherein the associated features comprise at least one of:

license plate hamming distance, vehicle attribute similarity, vehicle appearance similarity, time distance, period correlation characteristics, space Euclidean distance and space road network distance.

11. The method of claim 7, wherein the constructing a graph structure corresponding to each of the object images according to the image features and the associated features comprises:

taking an image feature corresponding to a first object image in the object images as a node feature of a vertex;

respectively taking the image characteristics corresponding to each second object image in the plurality of object images as the node characteristics of the neighbor nodes; wherein the second object image is an image other than the first object image among the plurality of object images;

calculating a feature weight between the first object image and each second object image according to the associated features between the first object image and each second object image;

determining edge features between the vertex and each neighbor node according to the associated features and feature weights between the first object image and each second object image;

and constructing a graph structure corresponding to the first object image by using the node characteristics of the vertex, the node characteristics of the neighbor nodes and the edge characteristics.

12. The method of claim 7, wherein said constructing a graph structure corresponding to each of said object images according to said image features and said associated features comprises:

taking an image feature corresponding to a first object image in the object images as a node feature of a vertex;

respectively taking the image characteristics corresponding to each third object image in the plurality of object images as the node characteristics of the neighbor nodes; the device distance between the image acquisition device corresponding to the third object image and the image acquisition device corresponding to the first object image meets a preset condition;

determining edge features between the top point and each neighbor node according to the association features between the first object image and each third object image;

13. The method according to claim 6, wherein the clustering the graph structures corresponding to the plurality of object images based on the preset graph neural network to obtain at least one group of graph structures comprises:

inputting each graph structure into the preset graph neural network respectively to obtain an embedded vector of each graph structure;

clustering a plurality of embedded vectors of the graph structure to obtain at least one group of embedded vector groups;

and determining a graph structure group corresponding to each group of embedded vectors based on the corresponding relation between the embedded vectors and the graph structures.

14. The method of claim 13, wherein said clustering a plurality of said graph-structured embedded vectors to obtain at least one set of embedded vector groups comprises:

calculating a vector distance between every two of the embedding vectors;

and clustering the embedded vectors according to the vector distance to obtain at least one group of embedded vector groups.

15. The method according to claim 6, wherein before clustering graph structures corresponding to the plurality of object images based on the preset graph neural network to obtain at least one group of graph structures, the method further comprises:

acquiring a plurality of groups of training samples; each group of training samples comprises a plurality of graph structure samples and a mark value corresponding to each graph structure sample, each graph structure sample corresponds to one image sample, and the mark value is used for representing the possibility that the graph structure sample and other graph structure samples belong to the same moving object;

and training the neural network of the graph by using a plurality of groups of training samples to obtain the neural network of the preset graph.

16. The method of claim 6, wherein the performing space-time point location matching on the moving features to obtain a driving trajectory of the moving object comprises:

inputting the space-time characteristics of the object images corresponding to each graph structure group into a preset sequence model for space-time point location matching to obtain the driving track of the moving object corresponding to each graph structure group; wherein the spatiotemporal features include an acquisition time of the object image and an acquisition place of the object image.

17. The method of claim 16, wherein the preset sequence model includes any one of a hidden markov model and a shortest euclidean distance model.

18. The method of claim 17, wherein the travel trajectory comprises a time series and a location series.

19. A method of data processing, comprising:

acquiring road data and image data of a moving object; wherein the moving object comprises a target vehicle;

acquiring image characteristics of the target vehicle based on the image data;

determining a movement characteristic of the target vehicle based on the image characteristic and the road data;

and performing space-time point location matching on the moving characteristics to obtain the driving track of the target vehicle.

20. The method of claim 19, wherein after the spatiotemporal point location matching of the moving features to obtain the driving trajectory of the target vehicle, the method further comprises:

and displaying the running track.

21. The method of claim 19, wherein after the spatiotemporal point location matching of the moving features to obtain the driving trajectory of the target vehicle, the method further comprises:

acquiring a real-time position of the target vehicle;

and adjusting the moving direction of the target vehicle based on the real-time position and the driving track.

22. A data processing apparatus comprising:

the first acquisition module is used for acquiring road data and image data of a moving object;

the first processing module is used for acquiring the image characteristics of the moving object based on the image data;

a second processing module for determining a movement feature of the moving object based on the image feature and the road data;

and the third processing module is used for carrying out space-time point location matching on the moving characteristics to obtain the driving track of the moving object.

23. A data processing apparatus comprising:

the second acquisition module is used for acquiring road data and image data of a moving object; wherein the moving object comprises a target vehicle;

the fourth processing module is used for acquiring the image characteristics of the target vehicle based on the image data;

a fifth processing module for determining a movement characteristic of the target vehicle based on the image characteristic and the road data;

and the sixth processing module is used for carrying out space-time point location matching on the moving characteristics to obtain the driving track of the target vehicle.

24. A data processing apparatus, characterized in that the apparatus comprises: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements a data processing method as claimed in any of claims 1-18 or claims 19-21.

25. A computer-readable storage medium, having stored thereon computer program instructions, which, when executed by a processor, implement a data processing method according to any one of claims 1-18 or claims 19-21.

Technical Field

The present invention belongs to the technical field of data processing, and in particular, to a data processing method, apparatus, device, and medium.

Background

An Intelligent Transportation System (ITS) is a comprehensive transportation System which effectively and comprehensively applies advanced scientific technology to the whole transportation management System, thereby ensuring safety, improving efficiency, improving environment and saving energy. The driving path of a moving object (such as a vehicle) is an important data information in an intelligent transportation system, and the path identification of the moving object is to restore the actual driving track of the moving object.

In the prior art, a travel track of a moving object is generally restored by using Global Positioning System (GPS) data. However, since the source of the GPS data is single and the GPS data does not cover the entire moving object, the restored moving object has a traveling track that is not comprehensive enough, and the traveling track of the moving object on the urban road cannot be reliably restored.

Disclosure of Invention

Embodiments of the present invention provide a data processing method, apparatus, device, and medium, which can improve comprehensiveness and reliability of a restored travel track of a moving object.

In a first aspect, an embodiment of the present invention provides a data processing method, including:

acquiring road data and image data of a moving object;

acquiring image characteristics of a moving object based on the image data;

determining a movement feature of the moving object based on the image feature and the road data;

and performing space-time point location matching on the moving characteristics to obtain the driving track of the moving object.

In a second aspect, an embodiment of the present invention provides a data processing method, including:

acquiring road data and image data of a moving object; wherein the moving object comprises a target vehicle;

acquiring image characteristics of the target vehicle based on the image data;

determining a movement characteristic of the target vehicle based on the image characteristic and the road data;

and performing space-time point location matching on the moving characteristics to obtain the driving track of the target vehicle.

In a third aspect, an embodiment of the present invention provides a data processing apparatus, including:

the first acquisition module is used for acquiring road data and image data of a moving object;

the first processing module is used for acquiring the image characteristics of the moving object based on the image data;

the second processing module is used for determining the movement characteristics of the moving object based on the image characteristics and the road data;

and the third processing module is used for carrying out space-time point location matching on the moving characteristics to obtain the driving track of the moving object.

In a fourth aspect, an embodiment of the present invention provides a data processing apparatus, including:

the second acquisition module is used for acquiring road data and image data of a moving object; wherein the moving object comprises a target vehicle;

the fourth processing module is used for acquiring the image characteristics of the target vehicle based on the image data;

the fifth processing module is used for determining the moving characteristics of the target vehicle based on the image characteristics and the road data;

and the sixth processing module is used for carrying out space-time point location matching on the moving characteristics to obtain the driving track of the target vehicle.

In a fifth aspect, an embodiment of the present invention provides a data processing apparatus, where the apparatus includes: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements a data processing method as described in the first aspect or the second aspect.

In a sixth aspect, the present invention provides a computer-readable storage medium, on which computer program instructions are stored, and when executed by a processor, the computer program instructions implement the data processing method according to the first aspect or the second aspect.

The data processing method, device, equipment and medium of the embodiment of the invention can restore the driving track of the moving object by utilizing the road data and the image data of the moving object acquired by the image acquisition equipment in the urban road, particularly can determine the moving characteristics of the moving object based on the fusion analysis of the image data and the road data, therefore, the space-time point location matching can be carried out on the moving characteristics of the moving object to obtain the driving track of the moving object, therefore, only image acquisition equipment which is installed in urban traffic needs to be passed, that is, the travel locus of the moving object in the urban traffic can be restored, the comprehensiveness and reliability of the restored travel locus of the moving object can be improved, and in addition, since it is not necessary to add a positioning device to the moving object, the cost for restoring the travel track of the moving object can also be reduced.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a system architecture diagram of a trajectory reduction system provided by an embodiment of the present invention;

FIG. 2 is a flow chart of a data processing method according to an embodiment of the present invention;

FIG. 3 is a flow chart illustrating a driving trajectory recovery process according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention;

fig. 5 is a schematic hardware configuration diagram of a data processing device according to an embodiment of the present invention.

Detailed Description

Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

In the prior art, the travel track of a moving object is generally restored by using GPS data, for example, GPS route restoration method. The GPS path reduction method utilizes GPS data to perform map matching on the GPS data based on a hidden Markov model or a shortest Euclidean distance, and then reduces the driving track of the mobile object according to a shortest path algorithm.

However, since the source of the GPS data is single and the GPS data does not cover the entire moving object, the restored moving object has a traveling track that is not comprehensive enough, and the traveling track of the moving object on the urban road cannot be reliably restored.

Taking the moving object as the driving vehicle in the urban road as an example, not all the driving vehicles are equipped with the GPS positioning device, so the GPS data cannot cover the whole number of vehicles in the urban road, the driving track of the restored vehicle is not comprehensive enough, and the driving track of the vehicle in the urban road cannot be reliably restored.

In order to solve the problems in the prior art, an embodiment of the present invention provides a trajectory restoring system.

Fig. 1 is a system architecture diagram of a trajectory restoration system according to an embodiment of the present invention. As shown in fig. 1, the trajectory restoration system includes a plurality of cameras and a server. The cameras are respectively installed at different positions of the urban road, such as crossroads, traffic light intersections, two sides of the road, two sides of viaducts and the like. Specifically, the plurality of cameras may be surveillance cameras installed on urban roads.

The camera is used for collecting image data related to a moving object on an urban road, such as a monitoring image of the urban road. The server is used for acquiring the road data and the image data acquired by the camera, performing image processing on the image data to obtain the image characteristics of the mobile object, performing fusion analysis on the image characteristics and the road data to determine the mobile characteristics of the mobile object, and finally performing space-time point location matching on the mobile characteristics of the mobile object to obtain the driving track of the mobile object.

In the embodiment of the present invention, the server may obtain the road data from the internet, or may obtain the road data from a designated device, which is not limited herein.

In the embodiment of the present invention, the moving object may include any one of a person, an animal, a vehicle, and an aircraft moving on a city road, which is not limited herein.

Taking the moving object as a running vehicle in an urban road as an example, the camera is used for collecting vehicle image data related to the running vehicle on the urban road, such as a monitoring image of the urban road. The server is used for acquiring the road data and the vehicle image data acquired by the camera, performing image processing on the vehicle image data to obtain the image characteristics of the running vehicle, performing fusion analysis on the image characteristics and the road data to determine the moving characteristics of the running vehicle, and finally performing space-time point location matching on the moving characteristics of the running vehicle to obtain the running track of the running vehicle.

Therefore, the track restoration system shown in fig. 1 can restore the running track of the moving object in the urban traffic only by the camera installed in the urban traffic, so that the leakage rate is reduced, the comprehensiveness and the reliability of the restored running track of the moving object are improved, and in addition, because a positioning device is not required to be added on the moving object, the cost for restoring the running track of the moving object can be reduced.

In order to implement the trajectory recovery system, embodiments of the present invention provide a data processing method, apparatus, device, and medium. The following first describes a data processing method provided in an embodiment of the present invention.

Fig. 2 is a flowchart illustrating a data processing method according to an embodiment of the present invention.

In some embodiments of the invention, the method shown in FIG. 2 may be performed by a server, such as the server shown in FIG. 1. Wherein the server may be a high-performance electronic calculator for storing and processing data.

As shown in fig. 2, the data processing method may include:

s110, acquiring road data and image data of a moving object;

s120, acquiring image characteristics of the moving object based on the image data;

s130, determining the moving characteristics of the moving object based on the image characteristics and the road data;

and S140, performing space-time point location matching on the moving characteristics to obtain the driving track of the moving object.

In the embodiment of the invention, the driving track of the mobile object can be restored by utilizing the road data and the image data of the mobile object acquired by the image acquisition equipment in the urban road, and particularly, the moving characteristic of the mobile object can be determined based on the fusion analysis of the image data and the road data, so that the space-time point matching can be carried out on the moving characteristic of the mobile object, and the driving track of the mobile object can be obtained.

In the embodiment of the present invention, the moving object may include any one of a person, an animal, a vehicle, and an aircraft moving on a city road, which is not limited herein.

In some embodiments of the present invention, the server may obtain the road data from the internet or from a designated device. The road data may be data related to urban traffic driving conditions. For example, the road data may include road base information, road construction information, road regulation information, road congestion information, road closure information, and the like.

In some embodiments of the invention, image data of a moving object may be acquired by an image acquisition device. The image acquisition device can be a camera installed on an urban road, such as a bayonet monitoring camera or an electric police monitoring camera.

In some embodiments, the image capturing device may directly capture a plurality of object images, and the server may acquire the plurality of object images captured by the image capturing device and treat the plurality of object images as image data of the moving object.

In these embodiments, optionally, the server may further perform a duplicate removal process on the object image acquired by the same image acquisition device to obtain multiple object images after the duplicate removal, and use the multiple object images after the duplicate removal as image data of the moving object.

In other embodiments, the image capturing devices may capture videos, such as road monitoring videos, and the server may acquire the videos captured by each image capturing device and use a plurality of videos captured by a plurality of image capturing devices as the video data.

In these embodiments, optionally, a specific method of acquiring image data may include:

acquiring video data, wherein the video data comprises a plurality of videos related to a moving object, and each video is acquired by image acquisition equipment;

and performing image interception on the video aiming at each video to obtain a plurality of object images.

Specifically, taking one video as an example, the server may first cut each image frame in the video, then cut the object images corresponding to all moving objects appearing in the image frame in each image frame, thereby obtaining a plurality of object images, and take the plurality of object images as image data.

For example, if a plurality of different moving objects appear in one image frame, an object image corresponding to each moving object in the image frame may be intercepted.

Taking a moving object as a running vehicle on an urban road as an example, the video may be a monitoring video collected by a camera, and the server may first intercept each image frame in the monitoring video, and then intercept vehicle images corresponding to all running vehicles appearing in the image frame in each image frame, thereby obtaining a plurality of vehicle images, and taking the plurality of vehicle images as image data.

In some embodiments of the present invention, after the image capturing is performed on the video to obtain a plurality of object images, the data processing method may further include:

and carrying out duplication elimination processing on the plurality of object images to obtain the duplicated object images.

Specifically, the server may perform deduplication processing on the object images intercepted from the same video, for example, the object image appearing first in the multiple similar object images appearing within a preset time length may be retained, that is, the object image with the earliest acquisition time in the multiple similar object images within the preset time length may be retained.

The preset time length can be set according to actual needs, and in the preset time length, the moving object cannot move repeatedly on a road or in an area corresponding to the shooting range of the image acquisition equipment.

Continuing to take the moving object as the driving vehicle on the urban road as an example, the server may perform deduplication processing on the vehicle images intercepted from the same surveillance video, for example, the vehicle image appearing in the first of the plurality of similar vehicle images appearing within the preset time duration may be retained, that is, the vehicle image with the earliest collection time among the plurality of similar vehicle images within the preset time duration may be retained. And in the preset time length, the running vehicle cannot repeatedly run on the road or in the area corresponding to the shooting range of the image acquisition equipment.

In these embodiments, the plurality of heavy vehicle images may be optionally used as the vehicle image data.

Thus, only the plurality of object images after the duplication removal can be subjected to image analysis processing, the data processing amount is reduced, and the data processing efficiency is improved.

In some embodiments of the present invention, the image data comprises a plurality of object images, each object image comprising a moving object.

Accordingly, S120 may include:

and performing image analysis on each object image to obtain image characteristics corresponding to the object images.

In some embodiments, for each object image, the specific method of S120 may include: the method comprises the steps of carrying out image recognition processing on a target image based on a target detection technology in computer vision so as to detect and extract information of a moving object in the target image, obtaining object characteristics of the moving object in the target image, and taking the object characteristics of the target image as image characteristics corresponding to the target image.

In other embodiments, for each object image, the specific method of S120 may further include: firstly, acquiring space-time characteristics of an object image, wherein the space-time characteristics comprise acquisition time of the object image and acquisition place of the object image, then, carrying out image recognition processing on the object image based on a target detection technology in computer vision to obtain object characteristics of a moving object in the object image, and finally, taking the object characteristics and the space-time characteristics as image characteristics corresponding to the object image.

Specifically, the spatiotemporal feature carried by the object image and the object feature of the moving object in the object image can be used as the image feature corresponding to the object image, so that the feature dimension of the image feature is improved, and the accuracy of the moving feature of the moving object obtained based on the image feature is improved.

In the embodiment of the present invention, the acquisition time of the object image may specifically refer to acquisition time of an image frame to which the object image belongs, and the acquisition location of the object image may specifically refer to an acquisition location corresponding to an image acquisition device that acquires a video to which the object image belongs.

Taking the moving object as the driving vehicle on the urban road as an example, the server may first obtain the temporal-spatial feature of the vehicle image including the acquisition time of the vehicle image and the acquisition location of the vehicle image, then perform image recognition processing on the vehicle image based on a target detection technology in computer vision to obtain the vehicle feature of the driving vehicle in the vehicle image, and finally, use the vehicle feature and the temporal-spatial feature as the image feature corresponding to the vehicle image.

In some embodiments of the present invention, in a case where the moving object includes a running vehicle and the object image includes a vehicle image, the object feature may include at least one of a license plate number feature, a vehicle attribute feature, and a vehicle appearance feature.

Wherein the license plate number characteristics include a license plate number. The vehicle attribute features include a vehicle type, a vehicle brand, and the like for distinguishing the vehicle type. The vehicle appearance features include vehicle color, vehicle texture, and the like for distinguishing the vehicle appearance.

In other embodiments of the present invention, in the case where the mobile object includes a walking person and the object image includes a person image, the object characteristics may include at least one of a person wearing characteristic, a person body shape characteristic, and a person five-sense-organ characteristic.

In some embodiments of the present invention, the specific method of S130 may be: clustering feature vectors formed by a plurality of image features to obtain at least one group of image features, wherein each group of image features corresponds to a moving object, establishing a road directed graph of an urban road based on road data, and matching one group of image features corresponding to each moving object with the road directed graph to obtain the moving features of each moving object.

In other embodiments of the present invention, the specific method of S130 may further include:

constructing a graph structure corresponding to each object image by using image features and road data corresponding to a plurality of object images;

clustering graph structures corresponding to the object images based on a preset graph neural network to obtain at least one graph structure group; wherein each group of graph structure groups is used as a moving feature of a moving object.

Specifically, the road network characteristics corresponding to each object image may be determined based on the road data, a graph structure corresponding to each object image may be constructed according to the image characteristics and the road network characteristics corresponding to the plurality of object images, and then the graph structures may be clustered based on a preset graph neural network, so as to obtain a graph structure group serving as the moving characteristics of each moving object.

In some embodiments of the present invention, a specific method for constructing a graph structure corresponding to each object image by using image features corresponding to a plurality of object images and road data may include:

according to the road data, establishing a road directed graph of the urban road;

for each object image, matching the object image with the road network directed graph according to the space-time characteristics of the object image to obtain road network characteristics corresponding to the object image; the space-time characteristics comprise the acquisition time of the object image and the acquisition place of the object image;

determining the association characteristics between every two object images according to the image characteristics and the road network characteristics;

and constructing a graph structure corresponding to each object image according to the image characteristics and the associated characteristics.

Specifically, a road directed graph of an urban road may be established based on road data, then according to an acquisition location of an image acquisition device to which each object image belongs, the image acquisition device is matched into the road directed graph according to an acquisition time sequence to determine road network features corresponding to the image acquisition device, thereby determining road network features corresponding to the object images acquired by each image acquisition device, then according to image features and road network features of every two object images, determining association features between the two object images, and finally, according to image features corresponding to a plurality of object images and association features between every two object images, constructing a graph structure corresponding to each object image.

In some embodiments of the invention, a road directed graph of urban roads may be built based solely on road data.

In other embodiments of the present invention, before the building a road directed graph of an urban road according to the road data, the data processing method may further include:

acquiring network traffic data and GPS data; wherein the GPS data comprises at least one of image acquisition equipment GPS information and moving object GPS information on urban roads.

The network traffic data may include network information data related to traffic conditions of urban traffic, such as hot spot traffic event information in a social network platform, and emergency traffic event information in a news network platform, which are updated on the internet. The image capture device GPS information is used to mark the image capture device location on different road segments. The vehicle GPS information is used for determining the crowdedness degree of each road section in the time period corresponding to the video data.

In these embodiments, optionally, the specific method for building a road directed graph of an urban road according to road data may further include:

and establishing a road directed graph according to the road data, the network traffic data and the GPS data.

Therefore, on the basis of a big data processing technology, comprehensive modeling can be performed by utilizing multi-source data consisting of network traffic data, GPS data and road data, a road directed graph is established, the maximum utilization of the obtained data is realized, and the accuracy and the reliability of the established road directed graph are improved.

In some embodiments of the present invention, a specific method for constructing a graph structure corresponding to each object image according to the image features and the associated features may include:

taking an image feature corresponding to a first object image in the plurality of object images as a node feature of a vertex;

respectively taking the image characteristics corresponding to each second object image in the plurality of object images as the node characteristics of the neighbor nodes; the second object image is an image other than the first object image in the plurality of object images;

calculating a feature weight between the first object image and each second object image according to the associated features between the first object image and each second object image;

determining edge characteristics between the top point and each neighbor node according to the associated characteristics and the characteristic weight between the first object image and each second object image;

and constructing a graph structure corresponding to the first object image by using the node characteristics of the vertex, the node characteristics of the neighbor node and the edge characteristics.

First, each of the plurality of object images may be regarded as a first object image, the first object image may be regarded as a vertex of the graph structure, and an image feature corresponding to the first object image may be regarded as a node feature of the vertex. Then, all the object images except the first object image may be respectively used as second object images, each second object image may be respectively used as a neighboring node of the vertex, and an image feature corresponding to each second object image may be respectively used as a node feature of the neighboring node. Then, taking the first object image and the second object image as an example, different weights may be assigned to different types of associated features, and a sum of associated feature weights between the first object image and the second object image is calculated, so that the sum of associated feature weights is used as a feature weight between the first object image and the second object image, and further the associated feature and the feature weight between the first object image and the second object image are used as an edge feature of an edge between a vertex and a neighboring node corresponding to the second object image. Finally, a graph structure corresponding to the first object image can be constructed by using the node features of the vertex, the node features of each neighbor node and the edge features of each edge.

In other embodiments of the present invention, a specific method for constructing a graph structure corresponding to each object image according to the image features and the associated features may include:

taking an image feature corresponding to a first object image in the plurality of object images as a node feature of a vertex;

determining edge characteristics between the top point and each neighbor node according to the association characteristics between the first object image and each third object image;

and constructing a graph structure corresponding to the first object image by using the node characteristics of the vertex, the node characteristics of the neighbor node and the edge characteristics.

First, each of the plurality of object images may be regarded as a first object image, the first object image may be regarded as a vertex of the graph structure, and an image feature corresponding to the first object image may be regarded as a node feature of the vertex. Then, third object images acquired by image acquisition equipment with an equipment distance between the third object images and the image acquisition equipment for acquiring the first object images meeting preset conditions can be acquired, each third object image is respectively used as a neighbor node of a vertex, and image features corresponding to each third object image are respectively used as node features of the neighbor nodes. Then, taking the first object image and a third object image as an example, the association feature between the first object image and the third object image may be used as an edge feature of an edge between the vertex and a neighboring node corresponding to the third object image. Finally, a graph structure corresponding to the first object image can be constructed by using the node features of the vertex, the node features of each neighbor node and the edge features of each edge.

Specifically, the preset condition may include that the device distance is smaller than a preset distance threshold, or the movement time corresponding to the device distance is smaller than a preset time threshold, or the device distance is the first N shortest image capturing devices in all the image capturing devices except the image capturing device corresponding to the first object image, where N is a positive integer.

In some embodiments of the invention, where the moving object comprises a moving vehicle and the object image comprises a vehicle image, the associated feature may comprise at least one of:

The license plate Hamming distance refers to the Hamming distance between license plate numbers. The vehicle attribute similarity refers to a similarity between the above-described features for distinguishing the vehicle types, and may be, for example, a sum of similarity weights of the respective vehicle attribute features. The vehicle appearance similarity refers to a similarity between the above-described features for distinguishing the vehicle appearance, and may be, for example, a sum of similarity weights of the respective vehicle appearance features. The temporal distance refers to the plausibility of a spatio-temporal relationship between two vehicle images, and may be determined using a gaussian mixture model, for example, based on spatio-temporal characteristics of the two vehicle images. The cycle related feature refers to a traffic flow of a link between two vehicle images. The spatial euclidean distance refers to the spatial euclidean distance between the location features corresponding to the two vehicle images. The spatial road network distance refers to a spatial road network distance between the location features corresponding to the two vehicle images determined based on the road network directed graph.

Therefore, the data mining technology can be utilized, the vehicle movement and transfer rules contained in the data can be mined, the matching rationality of the two vehicle images between the road network points can be deduced from multiple angles, and the accuracy of the driving track of the driving vehicle determined based on the vehicle movement characteristics can be improved.

In other embodiments of the present invention, where the mobile object comprises a walking person and the object image comprises a person image, the associated feature may comprise at least one of:

the figure wearing similarity, the figure body shape similarity and the figure five-sense-organ similarity.

In some embodiments of the present invention, a specific method for obtaining at least one group of graph structures by clustering graph structures corresponding to a plurality of object images based on a preset graph neural network may include:

inputting each graph structure into a preset graph neural network respectively to obtain an embedded vector of each graph structure;

clustering the embedded vectors of the graph structures to obtain at least one group of embedded vector groups;

and determining the graph structure group corresponding to each group of embedded vector groups based on the corresponding relation between the embedded vectors and the graph structures.

Specifically, after obtaining the embedded vector corresponding to each graph structure through a preset graph neural network, the embedded vectors may be clustered by using an existing vector clustering method to obtain at least one group of embedded vector groups, and finally, the graph structure group corresponding to each group of embedded vector groups may be determined based on the correspondence between the embedded vectors and the graph structures because one embedded vector corresponds to one graph structure.

Optionally, the clustering process is performed on the embedded vectors of the plurality of graph structures, and a specific method for obtaining at least one group of embedded vector groups may include:

calculating the vector distance between every two embedded vectors;

and clustering the plurality of embedded vectors according to the vector distance to obtain at least one group of embedded vector groups.

Specifically, after obtaining the vector distance between every two embedded vectors, every two embedded vectors whose vector distance is smaller than a preset vector distance threshold may be classified into one class, thereby obtaining at least one embedded vector group.

In some embodiments of the present invention, before clustering graph structures corresponding to a plurality of object images based on a preset graph neural network to obtain at least one group of graph structures, the data processing method may further include:

and (5) training the neural network of the graph by using a plurality of groups of training samples to obtain the neural network of the preset graph.

Specifically, the label values of the multiple graph structure samples in each set of training samples may be 0 or 1, where 0 represents that the graph structure sample and other graph structure samples do not belong to the same moving object, and 1 represents that the graph structure sample and other graph structure samples belong to the same moving object, first, the graph structure samples in each set of training samples are sequentially input to a graph neural network to obtain an embedded vector sample corresponding to each graph structure sample, then, each set of training samples are subjected to clustering processing, so as to determine whether each graph structure sample and other graph structure samples in each set belong to the same moving object according to a clustering result to obtain a prediction result, and finally, according to the prediction result and the label value of each graph structure sample, model parameters of the graph neural network are adjusted to obtain a preset graph neural network.

In S140 according to some embodiments of the present invention, the movement characteristics corresponding to each moving object may be input into a preset sequence model to perform space-time point matching processing, so as to obtain the driving trajectory of each moving object.

Optionally, the specific method of S140 may include:

inputting the space-time characteristics of the object images corresponding to each group of graph structure groups into a preset sequence model for space-time point location matching to obtain the driving track of the moving object corresponding to each group of graph structure groups; wherein the spatiotemporal features include an acquisition time of the object image and an acquisition place of the object image.

Therefore, the reasonability of the moving object moving between each point position of the road can be deduced according to the space-time characteristics of the object images corresponding to each group of the graph structure group based on the preset sequence model, so that a large-scale and fine-grained driving track is restored.

In some embodiments of the invention, the preset sequence model may comprise any one of a hidden markov model and a shortest euclidean distance model.

In some embodiments of the present invention, the travel track may include a time series and a location series, wherein one time and one location constitute one path node in the travel path, and thus, a plurality of path nodes traveled according to the time series and the location series may constitute the travel track of the mobile object.

Fig. 3 is a flowchart illustrating a driving trajectory recovery process according to an embodiment of the present invention. As shown in fig. 3, the driving trajectory restoration process may include:

1. the server can firstly acquire video data, GPS data, road data and network traffic data;

2. extracting image characteristics of different vehicle images based on video data, and meanwhile, establishing a road directed graph based on GPS data, road data and network traffic data;

3. carrying out data fusion on each vehicle image and the road directed graph, and matching each vehicle image to different camera nodes in the road directed graph;

4. carrying out data mining by using the image characteristics of each vehicle image and the road directed graph to obtain the association characteristics between every two vehicle images, and constructing a graph structure of each vehicle image based on the image characteristics and the association characteristics;

5. and (3) utilizing a deep learning model, such as a graph neural network and a sequence model, to fuse multi-class characteristics to carry out matching degree analysis on the samples of different space-time points, restoring the running tracks of a plurality of running vehicles in the video data according to matching rationality, and finding the running tracks of the whole number of running vehicles.

In summary, the embodiment of the present invention provides a complete and end-to-end driving trajectory reduction method, which can automatically reduce the driving trajectories of the driving vehicles in the urban traffic under the conditions of complete data driving and no manual work. The multi-source data can be fused and applied to the running track restoration, and the frontmost deep learning technology is utilized, so that the running track restoration accuracy can be improved. Therefore, the driving track of the running vehicle in the urban traffic can be restored only by the camera installed in the urban traffic, and the comprehensiveness and reliability of the restored driving track of the vehicle can be improved. In addition, because a positioning device does not need to be added on the vehicle, the cost for restoring the running track of the vehicle can be reduced.

The embodiment of the invention also provides a data processing method, which comprises the following steps:

acquiring road data and image data of a moving object; wherein the moving object comprises a target vehicle;

acquiring image characteristics of the target vehicle based on the image data;

determining a movement characteristic of the target vehicle based on the image characteristic and the road data;

and performing space-time point location matching on the moving characteristics to obtain the driving track of the target vehicle.

In some embodiments of the invention, real-time road data and image data can be acquired, data processing is performed on the image data in real time to obtain image characteristics of a target vehicle, then fusion analysis processing is performed on the image characteristics and the road data to automatically determine moving characteristics of the target vehicle, and finally space-time point matching processing is performed on the moving characteristics to obtain a driving track of the target vehicle. Therefore, the driving track of the target vehicle can be restored in real time quickly only by the camera installed in the urban traffic, and the target vehicle can be tracked in real time.

Taking a suspected vehicle tracking scene as an example, real-time road data and image data can be acquired, data processing is performed on the image data in real time to obtain image features of the suspected vehicle, then fusion analysis processing is performed on the image features and the road data to automatically determine moving features of the suspected vehicle, and finally space-time point location matching processing is performed on the moving features to obtain a driving track of the suspected vehicle. Therefore, the driving track of the suspected vehicle can be quickly restored in real time only by the aid of the installed camera in the urban traffic, and real-time tracking of the suspected vehicle is achieved.

Taking a scene that a user tracks and positions a vehicle (such as a taxi, a bus and the like) taken by the user as an example, real-time road data and image data can be obtained, data processing is carried out on the image data in real time to obtain image characteristics of the vehicle, then, fusion analysis processing is carried out on the image characteristics and the road data to automatically determine moving characteristics of the vehicle, and finally, space-time point matching processing is carried out on the moving characteristics to obtain a driving track of the vehicle. Therefore, the driving track of the vehicle can be restored in real time quickly only by the camera installed in the urban traffic, so that the vehicle can be tracked in real time.

In other embodiments of the present invention, historical road data and image data may be obtained, the image data is subjected to data processing to obtain image features of the target vehicle, then the image features and the road data are subjected to fusion analysis processing to automatically determine moving features of the target vehicle, and finally, the moving features are subjected to spatio-temporal point location matching processing to obtain a driving track of the target vehicle. Therefore, the running track of the target vehicle can be quickly restored only by the camera installed in the urban traffic, and the reliability of the restored running track of the target vehicle can be improved.

In some embodiments of the present invention, after obtaining the driving trajectory of the target vehicle, the data processing method may further include:

and displaying the running track.

For example, in the process of tracking the running track of the target vehicle in real time, the real-time running track of the target vehicle can be displayed according to the real-time data processing result, so that the visibility of the running track is improved.

In the case where the travel track includes a time series and a place series, point locations on a map of urban traffic may be matched based on the time series and the place series, so that the travel track of the target vehicle is displayed on the map in real time.

In some embodiments of the present invention, after obtaining the driving trajectory of the target vehicle, the data processing method may further include:

acquiring a real-time position of a target vehicle;

and adjusting the moving direction of the target vehicle based on the real-time position and the running track.

Taking an automatic driving scene as an example, the automatic driving navigation system may acquire a real-time position of the target vehicle, then perform analysis by using the real-time position and the driving track, determine a current position and a next position of the target vehicle in the driving track, and adjust a moving direction of the target vehicle in real time based on a driving speed of the target vehicle, the current position, and a direction of the next position, so as to realize automatic driving control of the target vehicle.

The target vehicle can be a vehicle installed on the automatic driving navigation system, and also can be a target vehicle which has control authority and is provided with vehicle-mounted equipment which is communicated with the automatic driving navigation system.

It should be noted that the method described in this embodiment is similar to the processes and effects in the method embodiments shown in fig. 2 and fig. 3, and the principle is similar, and is not repeated here to avoid repetition.

Fig. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention.

In some embodiments of the present invention, the apparatus shown in FIG. 4 may be located within a server, such as the server shown in FIG. 1. Wherein the server may be a high-performance electronic calculator for storing and processing data. As shown in fig. 4, the data processing apparatus 200 may include:

a first obtaining module 210 for obtaining road data and image data of a moving object;

a first processing module 220, configured to obtain an image feature of the moving object based on the image data;

a second processing module 230 for determining a movement characteristic of the moving object based on the image characteristic and the road data;

and the third processing module 240 is configured to perform space-time point location matching on the moving features to obtain a driving track of the moving object.

In some embodiments of the present invention, the image data includes a plurality of object images, each object image including a moving object therein;

optionally, the first processing module 220 may be specifically configured to:

and performing image analysis on each object image to obtain image characteristics corresponding to the object images.

In some embodiments of the present invention, the first processing module 220 may be further specifically configured to:

acquiring space-time characteristics of an object image; the space-time characteristics comprise the acquisition time of the object image and the acquisition place of the object image;

carrying out image recognition on the object image to obtain object characteristics of a moving object in the object image;

and taking the object characteristic and the space-time characteristic as the image characteristic corresponding to the object image.

In some embodiments of the present invention, the first obtaining module 210 may specifically be configured to:

acquiring video data, wherein the video data comprises a plurality of videos related to a moving object, and each video is acquired by image acquisition equipment;

and performing image interception on the video aiming at each video to obtain a plurality of object images.

In some embodiments of the present invention, the first obtaining module 210 may be further specifically configured to:

and carrying out duplication elimination processing on the plurality of object images to obtain the duplicated object images.

In some embodiments of the present invention, the second processing module 230 may specifically be configured to:

constructing a graph structure corresponding to each object image by using image features and road data corresponding to a plurality of object images;

In some embodiments of the present invention, the second processing module 230 may be further configured to:

establishing a road directed graph of the urban road according to the road data;

for each object image, matching the object image with the road network directed graph according to the space-time characteristics of the object image to obtain road network characteristics corresponding to the object image; the space-time characteristics comprise the acquisition time of the object image and the acquisition place of the object image;

determining the association characteristics between every two object images according to the image characteristics and the road network characteristics;

and constructing a graph structure corresponding to each object image according to the image characteristics and the associated characteristics.

In some embodiments of the present invention, the data processing apparatus 200 may further include:

the third acquisition module is used for acquiring network traffic data and GPS data; the GPS data comprises at least one of image acquisition equipment GPS information and moving object GPS information on urban roads;

optionally, the second processing module 230 may be further configured to:

and establishing a road directed graph according to the road data, the network traffic data and the GPS data.

In some embodiments of the invention, the moving object comprises a moving vehicle, the object image comprises a vehicle image, the object features comprise at least one of:

license plate number characteristics, vehicle attribute characteristics, and vehicle appearance characteristics.

In some embodiments of the invention, the association feature may comprise at least one of:

In some embodiments, the second processing module 230 may be further configured to:

taking an image feature corresponding to a first object image in the plurality of object images as a node feature of a vertex;

calculating a feature weight between the first object image and each second object image according to the associated features between the first object image and each second object image;

and constructing a graph structure corresponding to the first object image by using the node characteristics of the vertex, the node characteristics of the neighbor node and the edge characteristics.

In other embodiments, the second processing module 230 may be further configured to: