阅读说明：本技术 反刍动物监测系统 (Ruminant monitoring system ) 是由 宋翔宇 于 2020-05-04 设计创作，主要内容包括：提供了一种用于自动监测反刍动物的系统和方法。所述系统(1)包括3D相机系统(3),所述3D相机系统从感兴趣区域(10)获得图像。图像处理器(5)将所述感兴趣区域中的表面曲率确定为时间的函数。基于所述函数达到局部极大值的频率,生成所述动物的健康指示。(A system and method for automatically monitoring a ruminant animal is provided. The system (1) comprises a 3D camera system (3), which 3D camera system obtains images from a region of interest (10). An image processor (5) determines the surface curvature in the region of interest as a function of time. Generating an indication of the health of the animal based on the frequency at which the function reaches a local maximum.)

1. A system for automatically monitoring ruminants, in particular dairy animals such as cows, the system comprising:

a 3D camera system for obtaining a plurality of 3D images of at least one region of interest of the animal at successive points in time during at least a predetermined period of time,

-a control device connected to the 3D camera system, the control device being provided with

An image processing device for processing the obtained plurality of 3D images, an

-an output device for outputting the output data,

the control device being arranged to determine a health parameter frequency value of the health parameter based on the processed image and to output the health parameter frequency value to the output device,

wherein the image processing device is arranged to-determine a region of interest in the plurality of 3D images, an

-calculating a curvature value of the region of interest for each 3D image and determining the calculated curvature as a function of time,

wherein the control apparatus is arranged to determine:

-the point in time at which a local extremum of the function of time occurs, and

-said health parameter frequency value by analyzing said point in time with respect to a predetermined criterion.

2. The system of claim 1, wherein the 3D camera system comprises a time-of-flight camera or a structured light camera.

3. The system according to any preceding claim, wherein the curvature value comprises or is an average of curvature values of the region of interest, in particular an average averaged over a plurality of points across the region of interest.

4. A system according to any preceding claim, wherein the predetermined criterion is a frequency criterion, wherein the control apparatus is arranged to determine the frequency of the local extremum over time and to analyse the points in time by comparing the determined frequency with the frequency criterion.

5. A system according to claim 4, wherein the control apparatus is arranged to filter the function of the calculated curvature by filtering out temporal variations in the function of time having frequencies outside a predetermined frequency range.

6. The system of claim 5, wherein the control device is arranged to:

-performing a Fourier transform on the calculated curvature function to construct a transformed frequency function,

-removing all parts of the transformed frequency function outside the predetermined frequency range to obtain a clean frequency function, an

-determining the contraction frequency by analyzing the clean frequency function, in particular by determining the frequency value having the strongest signal value or the average of the frequency signals within the clean frequency function.

7. The system of any of claims 4 to 6, wherein the frequency criteria includes generating a health alert when the determined frequency is below a predetermined lower frequency threshold or above a predetermined upper frequency threshold.

8. System according to claim 7, wherein the lower frequency threshold and the higher frequency threshold are respectively-a historical value of the animal, in particular a historical average of the animal,

or

-a literature value of the animal, in particular depending on one or more of breed, age, days of lactation, or type of feed or feeding regime, respectively, of the animal.

9. A system according to claim 7 or 8, wherein the lower frequency threshold, the higher frequency threshold, respectively, depend on the type of activity the animal is performing, such as feeding, milking or resting.

10. The system of any preceding claim, comprising at least one animal station, each animal station selected from the group consisting of: milking station, feeding station, drinking station, treatment station, isolation station, calving pen and selection station,

wherein the 3D camera system comprises a 3D camera disposed in or on at least one of the stations.

11. The system of any preceding claim, wherein the health parameter is respiration and the region of interest is at least a portion of the thorax of the animal.

12. The system of claims 7 and 11, wherein the higher frequency threshold is out of 45 to 75 times per minute, inclusive.

13. The system according to any one of claims 1 to 10, wherein the health parameter is rumen motility and the region of interest is a left paraspinal fossa, in particular a partial region within the left paraspinal fossa.

14. The system of claims 7 and 13, wherein the higher frequency threshold is about 4 times per minute, and/or wherein the lower frequency threshold is about 0.5 times per minute.

15. The system of any one of claims 1 to 6, wherein the health parameter is labor contraction and the region of interest is the abdomen of the animal.

16. The system of claims 7 and 15, wherein the higher frequency threshold comes out of 5 to 10 times per hour.

17. A method for determining a health indication of a ruminant, in particular a cow, using a system according to any one of the preceding claims and comprising the steps of:

-obtaining a plurality of 3D images of at least one region of interest of the animal at successive points in time during at least a predetermined period of time,

-processing the obtained plurality of 3D images by the image processing device,

determining, by the image processing device, a region of interest in the plurality of 3D images,

calculating, by the control device, a curvature value of the region of interest for each 3D image and determining the calculated curvature as a function of time,

and determining:

-the point in time at which a local extremum of the function of time occurs, and

-said health parameter frequency value, by analyzing said point in time.