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Behavior Trajectory Tracking of Piglets Based on DLC-KPCA

Author

Listed:
  • 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
    Office of Model Animals, National Research Facility for Phenotypic and Genotypic Analysis of Model Animals, China Agricultural University, Beijing 100083, China)

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.

Suggested Citation

  • Chengqi Liu & Han Zhou & Jing Cao & Xuchao Guo & Jie Su & Longhe Wang & Shuhan Lu & Lin Li, 2021. "Behavior Trajectory Tracking of Piglets Based on DLC-KPCA," Agriculture, MDPI, vol. 11(9), pages 1-22, September.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:9:p:843-:d:627042
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    References listed on IDEAS

    as
    1. Paul Jackson & Abozar Nasirahmadi & Jonathan H. Guy & Steve Bull & Peter J. Avery & Sandra A. Edwards & Barbara Sturm, 2020. "Using CFD Modelling to Relate Pig Lying Locations to Environmental Variability in Finishing Pens," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    2. Svenja Opderbeck & Barbara Keßler & William Gordillo & Hansjörg Schrade & Hans-Peter Piepho & Eva Gallmann, 2020. "Influence of Increased Light Intensity on the Acceptance of a Solid Lying Area and a Slatted Elimination Area in Fattening Pigs," Agriculture, MDPI, vol. 10(3), pages 1-14, February.
    Full references (including those not matched with items on IDEAS)

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