Abstract
Tracking the behavior trajectories in pigs in group is becoming increasingly important for welfare feeding. A novel method was proposed in this study to accurately track individual trajectories of pigs in group and analyze their behavior characteristics. First, a multi-pig trajectory tracking model was established based on DeepLabCut (DLC) to realize the daily trajectory tracking of piglets. Second, a high-dimensional spatiotemporal feature model was established based on kernel principal component analysis (KPCA) to achieve nonlinear trajectory optimal clustering. At the same time, the abnormal trajectory correction model was established from five dimensions (semantic, space, angle, time, and velocity) to avoid trajectory loss and drift. Finally, the thermal map of the track distribution was established to analyze the four activity areas of the piggery (resting, drinking, excretion, and feeding areas). Experimental results show that the trajectory tracking accuracy of our method reaches 96.88%, the tracking speed is 350 fps, and the loss value is 0.002. Thus, the method based on DLC–KPCA can meet the requirements of identification of piggery area and tracking of piglets’ behavior. This study is helpful for automatic monitoring of animal behavior and provides data support for breeding.
Highlights
Piglets raised in a group is the mainstream form of livestock feeding at present, which can avoid chronic stress injury of confined pigs [1]
Based on kernel principal component analysis (KPCA) [32], this study introduced a high-dimensional spatiotemporal feature model to solve the nonlinear feature description problem of piglets
We introduce a novel method for target tracking and trajectory correcting based on DLC-KPCA to overcome the abovementioned issues
Summary
Piglets raised in a group is the mainstream form of livestock feeding at present, which can avoid chronic stress injury of confined pigs [1]. Piglets reared in a group have distinct activity and excretion areas and form their behaviors in fixed areas, such as lying down, excretion, and feeding [2,3,4]. Optimizing the activity area of piglets is helpful in the optimization of piggery and the welfare feeding of pigs [7,8]. The behavior trajectory tracking of piglets is directly related to their activity range and behavior expression. The accuracy of tracking multiple behavior trajectories must be high in a piggery with a relatively narrow space
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