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

Abstract

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.

Automatic identification and analysis of multi-object cattle rumination based on computer vision

“…Compared with the previous visual studies [16,17,20], the accuracy of this paper were 91.874%, we could see a slightly drop of accuracy, but the rumination of multi-object cattle could be identified basically without manual operation. The proposed method can achieve end-to-end automatic rumination identification of cattle.…”

“…It can also help determine whether to modify ration particle size according to the amount of time each cow spends ruminating, thereby achieving precise feeding and increasing the revenue earned by herders [9,10]. There are older studies on the automatic monitoring of rumination, which are mainly divided into two A C C E T E D 3 categories: one category identifies rumination by fitting animals with contacting monitoring devices [11][12][13][14][15], while the other monitors the animals via visual rumination monitoring programs [16][17][18][19][20].…”

Dairy Cow rumination detection: A deep learning approach

“…Another work by Li et al [33] on tracking multiple targets of cows to detect their mouth areas using optical flow technique achieved 89.12% of the tracking rate. The mean shift [34] and STC algorithms [35] were used by Chen et al [36,37] to monitor the rumination time using the optical flow technique to track the mouth movement of the cow. The monitoring process achieved 92.03% and 85.45% of accuracy, respectively.…”

“…CNN is another technique to extract features from images without any manual extraction. This technology is generally used for object detection [38] and visual action recognition [37,39]. D Li et al [39] used KELM [40] to identify mounting behavior of pigs.…”

Multi-Target Rumination Behavior Analysis Method of Cows Based on Target Detection and Optical Flow Algorithm

“…Chen et al employed the Mean-Shift algorithm to accurately track the movement of a cow's jaw and extracted the center-of-mass trajectory curve of the cow's mouth movement from six videos consisting of a total of 24,000 frames for monitoring cow rumination behavior [36]. Experimental results demonstrated 92.03% accuracy for this method.…”