Multi target pigs tracking loss correction algorithm based on Faster R-CNN

Longqing Sun, Yuanbing Zou, Yan Li, Zhengda Cai, Yue Li, Bing Luo, Yan Liu, Yiyang Li
2018 International Journal of Agricultural and Biological Engineering  
In order to solve the problem that target tracking frames are lost during the visual tracking of pigs, this research proposed an algorithm for multi target pigs tracking loss correction based on Faster R-CNN. The video of live pigs was processed by Faster R-CNN to get the object bounding box. Then, the SURF and background difference method were combined to predict whether the target pig will be occluded in the next frame. According to the occlusion condition, the maximum value of the horizontal
more » ... and vertical coordinate offset of the bounding box in the adjacent two frames of the frame image in continuous N (N is the value of the video frame rate) were calculated. When bounding boxes in a video frame are merged into one bounding box, this maximum value was used to correct the current tracking frame offset in order to achieve the purpose of solving the tracking target loss problem. The experiment results showed that the success rate range of RP Faster-RCNN in the data set was 80%-97% while in term of Faster-RCNN was 40%-85%. And the average center point error of RP Faster-RCNN was 1.46 lower than Faster-RCNN which was about 2.60. The new algorithm was characterized by good robustness and adaptability, which could solve the problem of missing tracking target and accurately track multiple targets when the targets occlude each other.
doi:10.25165/j.ijabe.20181105.4232 fatcat:qeuygvln5rfgvgo5ejvl5j4lwm