Abstract:
Abstract: There is a strong correlation between the ruminant behavior of dairy cows and their production, reproduction, estrus and stress behaviors. Rumination rhythm and time are related to the health status of dairy cows closely. By collecting and analyzing ruminant signals, it is possible to detect the physiological activities of dairy cows accurately and find the health problems of dairy cows in time. It is of great significance to improve the modern management level of dairy cows, promote the fine management of dairy cows' breeding, and improve the efficiency of pastures. The existing methods mostly use artificial observation or wearable devices to monitor the ruminant behavior of dairy cows, which has the problems of large error, being easy to cause stress reaction of dairy cows, high cost, low real-time performance, and so on. In the field of target recognition and tracking, the kernelized correlation filters (KCF) algorithm and the compressive tracking (CT) algorithm are widely used and have achieved good results, such as high real-time performance, high accuracy, effective suppression of tracking drift, high robustness, and good tracking effect. In order to achieve real-time multi-target monitoring of ruminant behaviors of dairy cows, by video analysis and target tracking technology, on the basis of obtaining the mouth area of dairy cows, the performances of CT algorithm and KCF algorithm in multi-target intelligent monitoring of cows ruminating were analyzed and compared in this study. To verify the effect of different algorithms on the monitoring of ruminant behavior of dairy cows, 9 videos were used to test and then compared with the actual ruminant data of cows, including 2 multi-target cow videos and 7 double-target cow videos. Additionally, aimed to the occurrence of missed detection, false detection, and so on, we proposed an effective judgment model for counting the number of chewing times. The test results showed that for multi-target monitoring, the average frame processing speed was 7.37 frames/s with the KCF algorithm, and was 0.51 frames/s with the CT algorithm; the average error of the KCF algorithm was 13.27 pixels, and that of the CT algorithm was 38.28 pixels; the average tracking error of the KCF algorithm was 34.67% of that of the CT algorithm. For double-target monitoring, the maximum false detection rate of the KCF algorithm was 18.42%, and the lowest was 0; the highest false detection rate of the CT algorithm was 81.58%, and the lowest was 0; the average false detection rate of the KCF algorithm was 7.72%, which was 10.84 percentage points lower than that of the CT algorithm. The frame processing speeds of the 2 algorithms were respectively 10.11 and 0.87 frames/s; the highest tracking errors were 45.80 and 46.13 pixels respectively, and the lowest were 7.71 and 17.33 pixels respectively. The average tracking error of the KCF algorithm was only 77.83% of that of the CT algorithm. The experimental results showed that for multi-target monitoring of rumination behavior of cows in a complex environment with the requirements of high accuracy and high real-time performance, the KCF algorithm with the low false detection rate and high frame processing speed was more suitable. On this basis, we verified the effectiveness of these 2 algorithms in monitoring ruminant behavior when cows were exposed to different lighting, and had different postures and different degrees of occlusion. The results showed that the CT algorithm had different degrees of deviation, and even lost the target, while the KCF algorithm still had good results and good adaptability in the nighttime video tracking. It shows that it is feasible and effective to apply the KCF algorithm to the all-day multi-target analysis of the ruminant behavior of dairy cows.