Behavior Trajectory Tracking of Piglets Based on DLC-KPCA

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...

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Bibliographic Details
Published in:Agriculture (Basel) 2021-09, Vol.11 (9), p.843
Main Authors: Liu, Chengqi, Zhou, Han, Cao, Jing, Guo, Xuchao, Su, Jie, Wang, Longhe, Lu, Shuhan, Li, Lin
Format: Article
Language:English
Subjects:
Accuracy
Aggressiveness
Algorithms
Animal behavior
Animal welfare
Behavior
behavior tracking
Clustering
DeepLabCut
Hogs
KPCA
Neural networks
piglets
Principal components analysis
Statistical analysis
Swine
Thermal mapping
Tracking
trajectory correction
Trajectory optimization
Unmanned aerial vehicles
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Summary: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.
ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture11090843