Automatic monitoring method of cow ruminant behavior based on spatio-temporal context learning

Automatic monitoring of cow rumination has great significance in the development of modern animal husbandry. In order to solve the problem of high real-time requirement of ruminant behavior monitoring, a tracking method based on STC (Spatio-Temporal Context) learning was carried out. On the basis of cow’s mouth region extraction, the spatial context model between target object and its local surrounding background was built based on their spatial correlations by solving the deconvolution problem, and the learned spatial context model was used to update the STC learning model for the next frame. Tracking in the next frame was formulated by computing a confidence map as a convolution problem that integrates the STC learning information, and the best object location could be estimated by maximizing the confidence map. Then the target scale was estimated based on the confidence evaluation. Finally, accurate tracking of the mouth movement trajectory was realized. To verify the effectiveness of the proposed method, the performance of the algorithm was tested using 20 video sequences. Besides, the tracking results were compared with the Mean-shift algorithm. The results showed that the average success rate of STC learning monitoring algorithm was 85.45%, which was 9.45% higher than the Mean-shift algorithm, the detection rate of STC learning monitoring algorithm was 18.56 s per video, which was 22.08% higher than that of the Mean-shift algorithm. The results showed that the fast tracking method based on STC learning monitoring algorithm is effective and feasible.