Behavior Trajectory Tracking of Piglets Based on DLC-KPCA

Chengqi Liu, Han Zhou, Jing Cao, Xuchao Guo, Jie Su, Longhe Wang, Shuhan Lu and Lin Li
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Chengqi Liu: Department of Computer Science and Technology, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Han Zhou: Department of Computer Science and Technology, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Jing Cao: Department of Computer Science and Technology, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Xuchao Guo: Department of Computer Science and Technology, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Jie Su: Department of Computer Science and Technology, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Longhe Wang: Office of Model Animals, National Research Facility for Phenotypic and Genotypic Analysis of Model Animals, China Agricultural University, Beijing 100083, China
Shuhan Lu: Department of Information, School of Information, University of Michigan, Ann Arbor, MI 48109, USA
Lin Li: Department of Computer Science and Technology, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2021, vol. 11, issue 9, 1-22

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.

Keywords: piglets; behavior tracking; trajectory correction; DeepLabCut; KPCA (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
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