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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks.

Kaifeng Zhang1, Dan Li1, Jiayun Huang1

  • 1College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.

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|February 22, 2020
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Summary

Automated pig behavior recognition using deep learning models accurately identifies activities like feeding and walking. The Temporal Segment Networks (TSN) model achieved 98.99% accuracy, offering an efficient alternative to manual monitoring.

Keywords:
deep learninginflated 3D convnetpig behaviortemporal segment networkstwo-stream convolutional network

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Area of Science:

  • Animal Science
  • Computer Vision
  • Machine Learning

Background:

  • Manual monitoring of pig behavior is labor-intensive and subjective.
  • Automated systems are needed for timely detection of pig health and welfare issues.
  • Deep learning offers potential for accurate pig behavior recognition.

Purpose of the Study:

  • To develop and evaluate deep learning models for automatic pig behavior recognition.
  • To incorporate both spatial and temporal features for enhanced accuracy.
  • To address limitations of existing methods that only use static image frames.

Main Methods:

  • Utilized a two-stream convolutional network approach, combining image frames and optical flow.
  • Implemented deep learning models: Inflated 3D ConvNet (I3D) and Temporal Segment Networks (TSN).
  • Employed ResNet and Inception architectures as feature extraction backbones for the models.
  • Created and used a standard dataset of 1000 pig videos covering five behaviors: feeding, lying, walking, scratching, and mounting.

Main Results:

  • The Temporal Segment Networks (TSN) model with ResNet101 backbone achieved the highest average recognition accuracy of 98.99%.
  • The best performing TSN model demonstrated an average recognition time of 0.3163 seconds per video.
  • Comparative experiments confirmed the superiority of the proposed TSN model over other evaluated models.

Conclusions:

  • The developed TSN model (ResNet101) effectively recognizes key pig behaviors with high accuracy and speed.
  • This automated approach significantly improves upon traditional methods for pig behavior monitoring.
  • The study highlights the potential of deep learning, particularly two-stream networks, for advancing precision livestock farming.