Real-time video quality diagnosis system and method
阅读说明:本技术 一种实时视频质量诊断系统及方法 (Real-time video quality diagnosis system and method ) 是由 张俊 万森 程腾 笪小六 陈小奇 刘琼 张敬锋 于 2019-09-30 设计创作,主要内容包括:本发明公开一种实时视频质量诊断系统及方法,包括视频源获取单元、视频质量诊断单元、数据存储单元、管理单元,视频源获取单元设备信息获得视频流地址,并将获得的视频流地址信息通过管理单元传输至用户端;视频质量诊断单元对由视频源获取单元获得的视频流地址进行一次校验诊断并生成诊断结果,同时保存异常图片至图片库,随后用户端通过终端页面查看并对异常图片进行二次校验比对准确性,当二次校验的诊断结果与一次校验的诊断结果不一致时,则将二次校验的诊断结果加入误检库,为自学习模块提供误检样本。本发明提供的自学习模块根据误检库中提供的误检样本数据对摄像机的算法阈值进行修正,以此提高自学习模块计算方法的准确性。(The invention discloses a real-time video quality diagnosis system and a real-time video quality diagnosis method, which comprise a video source acquisition unit, a video quality diagnosis unit, a data storage unit and a management unit, wherein the video source acquisition unit acquires a video stream address according to equipment information and transmits the acquired video stream address information to a user side through the management unit; the video quality diagnosis unit carries out primary check diagnosis on the video stream address obtained by the video source obtaining unit to generate a diagnosis result, meanwhile, the abnormal picture is stored in the picture library, then the user side checks through a terminal page and carries out secondary check comparison accuracy on the abnormal picture, when the diagnosis result of the secondary check is inconsistent with the diagnosis result of the primary check, the diagnosis result of the secondary check is added into the false check library, and a false check sample is provided for the self-learning module. The self-learning module provided by the invention corrects the algorithm threshold of the camera according to the false detection sample data provided in the false detection library, so that the accuracy of the calculation method of the self-learning module is improved.)
1. A real-time video quality diagnosis system comprises a video source acquisition unit (10), a video quality diagnosis unit (20), a data storage unit (30) and a management unit (40), and is characterized in that the video source acquisition unit (10) acquires a video stream address according to equipment information connected in a local area network and transmits the acquired video stream address information to a user side through the management unit (40);
the video quality diagnosis unit (20) performs primary check diagnosis on the video stream address obtained by the video source obtaining unit (10) to generate a diagnosis result, stores an abnormal picture to the picture library, then a user side checks the abnormal picture through a terminal page and performs secondary check comparison accuracy on the abnormal picture, and when the diagnosis result of the secondary check is inconsistent with the diagnosis result of the primary check, the diagnosis result of the secondary check is added to the false check library to provide a false check sample for the self-learning module;
the data storage unit (30) stores the diagnosis result processed by the video quality diagnosis unit (20), and the user side views the diagnosis result through a terminal page of the management unit (40);
the management unit (40) is an interactive module of a user side and a system.
2. The real-time video quality diagnosis system according to claim 1, wherein the video quality acquisition unit (10) searches for video monitoring device information in a local area network based on an ovif protocol, and acquires a video stream address according to device information connected in the local area network, the video stream address mainly including IP address information and port number of the video monitoring device.
3. The real-time video quality diagnostic system according to claim 2, wherein the video quality acquisition unit (10) logs in by verifying a user name and a password of the device.
4. The real-time video quality diagnostic system of claim 1, wherein the storing of the diagnostic results of the primary and secondary verification is based on MySQL implementations.
5. The real-time video quality diagnostic system according to claim 1, wherein a plurality of video quality diagnostic methods are provided in the video quality diagnostic unit (20), and the scene labels and corresponding output values are calculated from the diagnosis results of the video quality diagnostic methods.
6. The real-time video quality diagnostic system of claim 5, wherein the self-learning module self-learns from the secondarily-verified diagnostic results in the false positive library and calculates a generation algorithm threshold adjustment factor.
7. The real-time video quality diagnosis system of claim 6, wherein the self-learning module applies and calculates a corresponding video quality diagnosis method according to the diagnosed scene tag, and then compares the value of the diagnosis result with an algorithm threshold of the video quality diagnosis method, and decreases the algorithm threshold of the video quality diagnosis method if the value of the diagnosis result is less than the algorithm threshold, and increases the algorithm threshold of the video quality diagnosis method if the value of the diagnosis result is greater than the algorithm threshold.
8. The method of a real-time video quality diagnostic system of any one of claims 1-7, wherein the self-learning module self-learns the method as follows:
wherein α is more than 1, RiRepresents the diagnostic value of the false detection sample in the false detection library, mu is the set average value of the false detection sample, max and min represent the maximum value and the minimum value of the value obtained by the video quality diagnostic unit (20) corresponding to the false detection sample set, α is the obtained algorithm adjustment factor, moIs a preset threshold value, fixed and unchanged, meIs the adjusted algorithm threshold;
if α is less than 1, the reciprocal is directly taken and substituted into the formula (3) for calculation.
Technical Field
The invention belongs to the technical field of real-time video monitoring, and particularly relates to a real-time video quality diagnosis system and method.
Background
With the rapid development of computer vision technology, the application field of video monitoring systems is more and more extensive, and key information can be rapidly acquired through video monitoring so as to save a large amount of material resources and financial resources. When the video in monitoring is abnormal, if the video cannot be checked and solved in time, a large amount of key information will be lost, and therefore, the diagnosis of the video quality is of great importance.
The video quality diagnosis method comprises the steps of firstly obtaining video frames of cameras to be detected through a video source obtaining unit, sending the video frames to a video quality diagnosis unit, and diagnosing the video frames through a detection algorithm corresponding to each type of detection items by the video quality diagnosis unit according to the received video frames and algorithm thresholds of each type of detection items of each camera to be detected.
In video quality diagnosis, a diagnosis result is a judgment standard for measuring video quality in monitoring, but the performance of an algorithm is a certain bottleneck, a false detection phenomenon can be caused only by the diagnosis result of the algorithm, the conventional video quality diagnosis method cannot provide a secondary verification and self-learning mechanism, and the threshold value of the video quality diagnosis method is difficult to adjust aiming at the false detection result.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a real-time video quality diagnosis system and method, which solve the problems that a secondary verification and self-learning mechanism cannot be provided in the prior art, and the threshold value of the video quality diagnosis method is difficult to adjust according to the false detection result.
The purpose of the invention can be realized by the following technical scheme:
a real-time video quality diagnosis system comprises a video source acquisition unit, a video quality diagnosis unit, a data storage unit and a management unit, wherein the video source acquisition unit acquires a video stream address according to equipment information connected in a local area network and transmits the acquired video stream address information to a user side through the management unit;
the video quality diagnosis unit carries out primary check diagnosis on the video stream address obtained by the video source acquisition unit to generate a diagnosis result, stores an abnormal picture to the picture library, then a user side checks through a terminal page and carries out secondary check comparison on the abnormal picture to obtain accuracy, and when the diagnosis result of the secondary check is inconsistent with the diagnosis result of the primary check, the diagnosis result of the secondary check is added into the false check library to provide a false check sample for the self-learning module;
the data storage unit stores the diagnosis result processed by the video quality diagnosis unit, and the user side checks the diagnosis result through a terminal page of the management unit;
the management unit is an interactive module between the user side and the system.
Further, the video quality acquisition unit searches for video monitoring device information in the local area network based on an ovif protocol, and acquires a video stream address according to device information connected in the local area network, wherein the video stream address mainly includes IP address information and a port number of the video monitoring device.
Further, the video quality acquisition unit logs in through a user name and a password mode of the verification device.
Further, the storage of the diagnosis results of the primary check and the secondary check is realized based on MySQL.
Furthermore, a plurality of video quality diagnosis methods are arranged in the video quality diagnosis unit, and a scene label and a corresponding output value are calculated by the video quality diagnosis method according to the diagnosis result.
Further, the self-learning module carries out self-learning through the diagnosis result of the secondary verification in the false detection library, and calculates and generates an algorithm threshold value adjusting factor.
Further, the self-learning module is applied to the corresponding video quality diagnosis method according to the diagnosed scene label and calculates, then compares the value of the diagnosis result with the algorithm threshold of the video quality diagnosis method, if the value of the diagnosis result is smaller than the algorithm threshold, the algorithm threshold of the video quality diagnosis method is reduced, and if the value of the diagnosis result is larger than the algorithm threshold, the algorithm threshold of the video quality diagnosis method is improved.
Further, the calculation method of the self-learning module is as follows:
wherein α is more than 1, RiRepresenting the diagnostic value of the false detection sample in the false detection library, mu is the set average value of the false detection sample, max and min represent the maximum value and the minimum value of the value obtained by the false detection sample set corresponding to the video quality diagnostic unit, α is the adjustment factor of the obtained algorithm, moIs a preset threshold value, fixed and unchanged, meIs the adjusted algorithm threshold;
if α is less than 1, the reciprocal is directly taken and substituted into the formula (3) for calculation.
The invention has the beneficial effects that:
1. according to the invention, the video source acquisition unit acquires video resources from the monitoring equipment, the video quality diagnosis unit starts a video quality diagnosis method for the acquired video, the acquired diagnosis result is stored in the database, and the user side can check the result information of the video quality diagnosis through the management unit.
2. According to the method, the user side checks the abnormal picture through the terminal page through the added self-learning module, secondary verification is carried out on the abnormal picture, the comparison accuracy is compared, and if the video quality is normal and the video quality diagnosis unit diagnoses the abnormal picture, the diagnosis result of the secondary verification is added into the false detection library; and the self-learning module in the video quality diagnosis unit corrects the algorithm threshold of the camera according to the false detection sample data provided in the false detection library, so that the accuracy of the calculation method of the self-learning module is improved.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic block diagram of the system operation structure of an embodiment of the present invention;
FIG. 2 is a block diagram illustrating the working structure of a video source obtaining unit according to an embodiment of the present invention;
FIG. 3 is a block diagram of the video source diagnostic unit according to an embodiment of the present invention;
FIG. 4 is a block diagram illustrating the operation of a data storage unit according to an embodiment of the present invention;
FIG. 5 is a block diagram illustrating the operation of a management unit according to an embodiment of the present invention;
FIG. 6 is a block diagram of a self-learning module of an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a real-time video quality diagnosis system and method, as shown in fig. 1, including a video
As shown in fig. 2, the video
As shown in fig. 3, the video
As shown in fig. 4, the
As shown in fig. 5, in order to improve the accuracy of the real-time video quality diagnosis result, a self-learning module is added in the video
the above calculation method is explained in detail below, RiRepresenting the diagnostic value of the false detection sample, μ is the mean value of the false detection sample set, max and min represent the maximum and minimum values of the value obtained by the video quality
As shown in fig. 6, the
In summary, in the real-time video quality diagnosis system provided by the present invention, the video source obtaining unit obtains the video resource from the monitoring device, the video quality diagnosis unit starts the video quality diagnosis method for the obtained video, the obtained diagnosis result is stored in the database, and the user side can directly view the result information of the video quality diagnosis through the management unit. And meanwhile, the provided self-learning module corrects the algorithm threshold of the camera according to the data in the false detection library, so that the accuracy of the video quality diagnosis method is improved.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
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