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Motion-Status-Driven Piglet Tracking Method for Monitoring Piglet Movement Patterns Under Sow Posture Changes.

Aqing Yang1, Shimei Li2, Shuqin Tu3

  • 1College of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.

Veterinary Sciences
|July 25, 2025
PubMed
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This summary is machine-generated.

A new MSHMTracker system accurately monitors piglet movement around sows, improving safety and farm productivity. This automated tracking helps prevent piglet crushing and provides insights into sow-piglet interactions.

Area of Science:

  • Animal Science
  • Computer Vision
  • Agricultural Engineering

Background:

  • Piglet safety and healthy growth depend on understanding their movement around sows during posture changes.
  • Automated monitoring systems can reduce farm labor and prevent piglet crushing incidents.
  • Existing Joint Detection-and-Tracking (JDT)-based methods face challenges with piglet misidentification and tracking loss due to occlusion and crowding.

Purpose of the Study:

  • To develop an advanced piglet tracking system, MSHMTracker, to overcome limitations of current methods.
  • To improve the accuracy and reliability of automated monitoring in piglet-sow environments.
  • To analyze piglet behaviors and stress responses in relation to sow posture changes.

Main Methods:

  • Developed MSHMTracker with a motion-status hierarchical architecture and a score- and time-driven hierarchical matching mechanism (STHM).
Keywords:
behavioral patternshierarchical matching mechanismmulti-object trackingsocial relationshipsstress behavior recognition

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  • Utilized STHM for spatio-temporal association based on piglet motion status to enhance tracking robustness.
  • Identified piglet aggregation and dispersion behaviors using tracked trajectory data in response to sow posture changes.

    • Main Results:

    • MSHMTracker achieved 93.8% tracking accuracy (MOTA) and 92.9% identity consistency (IDF1) on over 30,000 images.
    • The system outperformed six popular tracking systems in performance.
    • Behavior recognition accuracy reached a mean of 87.5%, with significant correlations (0.6 and 0.82) found between piglet stress and sow posture changes.

    Conclusions:

    • MSHMTracker significantly improves piglet tracking accuracy and reliability in complex farm environments.
    • The study provides valuable insights into sow-piglet relationships and piglet stress responses.
    • This technology has the potential to enhance animal husbandry productivity and reduce labor costs for farmers.