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Related Experiment Video

Updated: Apr 30, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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BCST-GCN: a skeleton-based spatiotemporal graph convolutional network with bidirectional cross-attention for pig

Haojie Chai1, Weibo Zhan2, Jianshuai Su1

  • 1College of Artificial Intelligence, Henan Institute of Science and Technology, Xinxiang, China.

Frontiers in Veterinary Science
|April 29, 2026
PubMed
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This study introduces a new skeleton-based method for pig behavior recognition, improving accuracy and robustness. The advanced model enhances spatiotemporal feature extraction for intelligent farming applications.

Area of Science:

  • Computer Vision
  • Animal Behavior Analysis
  • Machine Learning

Background:

  • Existing video-based pig behavior recognition methods struggle with weak inter-frame motion correlation and poor robustness.
  • Current approaches often fail to fully exploit skeleton spatiotemporal dynamic features and capture fine behavioral details.

Purpose of the Study:

  • To propose a novel skeleton-based spatiotemporal dynamic modeling method for enhanced pig behavior recognition.
  • To improve the accuracy, precision, and recall in recognizing pig behaviors like feeding, walking, and lying.

Main Methods:

  • Utilized DeepLabCut (DLC) for accurate pig skeleton keypoint extraction and topological structure construction.
  • Streamlined the Spatiotemporal Graph Convolutional Network (ST-GCN) by removing redundant layers.
Keywords:
BCST-GCNDeepLabCutattention mechanismgraph convolutional networkpig behavior recognitionpose estimation

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  • Developed an improved BCST-GCN model incorporating a global-local self-attention BC module for dynamic topological correlation reconstruction.

    • Main Results:

    • The proposed framework effectively recognized typical pig behaviors including feeding, walking, lying, and dog-sitting posture.
    • The improved BCST-GCN model demonstrated significant gains: 6.94% in accuracy, 5.61% in precision, and 6.88% in recall compared to the baseline.

    Conclusions:

    • The developed method offers accurate and efficient pig behavior recognition, overcoming limitations of weak temporal correlation and insufficient feature extraction.
    • Provides a reliable technical solution for intelligent monitoring in pig farming, supporting the industry's intelligent upgrading.