# Research on Tracking and Identification of Typical Protective Behavior of Cows Based on DeepLabCut
###### Abstract
Some scholars have completed the recognition of key behaviors of the head and trunk of cows [4], but there is still a great practical need for continued research regarding the detailed recognition of cow protective behavior. The automatic recognition of animal behaviors with the help of machine vision technology can provide the basis for psychological and physiological analysis and evaluation of animals [5]. Diary cows usually live in outdoor or semi-enclosed barns. Most of the research on the recognition of dairy cow behaviors has been directed towards the detection of motion targets [6]. [PERSON] et al. proposed the use of the Viola-Jones algorithm for the recognition of the lying posture of cows using multiple cameras [7]. [PERSON] et al. used the automatic matching circle arc of back posture to determine the lame cow target based on curvature [8]. However, the detection of motion targets regarding cow protective behavior in dynamic backgrounds is still a challenging topic due to the global motion of the background [9], the illumination variation of the background, background noise, scale variation of the target, and obscuration [10]. Deep learning neural networks have profoundly influenced many areas of artificial intelligence [11], especially the very challenging image recognition tasks [12; 13]. The human behavior detection technology based on machine vision technology has made great progress, and more and more valuable research results have emerged. These research results provide a reference solution for the implementation of this study. We can learn from the current development of excellent algorithms for use in the field of human pose estimation. Some excellent algorithms can also be applied to cow protective behavior detection. We believe that an effective way to achieve complex protective behavior recognition is to fuse apparent features, such as shape, texture, and color, with multiple features, such as the motion features of optical flow, velocity, and motion trajectory, as well as depth information. There are many excellent algorithms availavle, including DeeperCut [14], ArtTrack [15], DeepPose [16], etc., which are effective in human pose tracking, but these methods do not study the behavioral characteristics of animals deeply enough and are not well applied in animal behavior detection [17]. Some scholars have made preliminary attempts to use the DeepLabCut technique in the behavior detection of rats and insects [18]. DeepLabCut is a novel deep learning algorithm developed by a team at Harvard University that can track animal movements, as well as behaviors, with artificial levels of accuracy and without the use of tracking markers or time-consuming manual analysis [19]. [PERSON] et al. developed an algorithm based on the optimized DeepLabCut algorithm that can automatically track the pest trajectory of key points and accurately identify their behaviors [20]. [PERSON] et al. used the DeepLabCut algorithm to study articulatory sites, which is found to provide unique kinematic data for the tongue, hyoid bone, jaw, and lips [21]. In this paper, we will investigate a behavior recognition algorithm based on the DeepLabCut target detection algorithm that monitors changes in the location of key parts of cows to identify head swinging, walking, and other protective behaviors of cows.