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AnimalAI: An Open-Source Web Platform for Automated Animal Activity Index Calculation Using Interactive Deep Learning

Mahtab Saeidifar1,2, Guoming Li1,2,3, Lakshmish Macheeri Ramaswamy3

  • 1Institute for Artificial Intelligence, Franklin College of Arts and Sciences, University of Georgia, Athens, GA 30602, USA.

Animals : an Open Access Journal From MDPI
|August 14, 2025
PubMed
Summary

This study introduces an open-source platform using advanced AI to accurately track animal activity from videos. It offers a user-friendly solution for researchers to analyze animal behavior and welfare efficiently.

Keywords:
activity indexanimal behaviordeep learningsoftware

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Area of Science:

  • Animal Behavior and Welfare Science
  • Computer Vision and Machine Learning
  • Bioinformatics and Computational Biology

Background:

  • Accurate animal activity monitoring is vital for welfare and behavior studies.
  • Traditional methods for activity index calculation are noisy, inaccurate, and lack user-friendly group/individual tracking.
  • Existing solutions lack open-access platforms for non-technical researchers.

Purpose of the Study:

  • To develop an open-source, web-based platform for calculating animal activity index from top-view videos.
  • To enable user-friendly selection of individual or group animals for tracking.
  • To integrate advanced AI for accurate, annotation-free animal tracking.

Main Methods:

  • Developed an open-source web platform for activity index calculation.
  • Integrated the Segment Anything Model 2 (SAM2) for promptable, deep learning-based animal segmentation and tracking.
  • Validated the platform using Cobb 500 male broilers (weeks 1-7), tracking 1157 chickens.

Main Results:

  • Achieved 100% tracking success rate with high accuracy (IoU: 92.21%, Precision: 93.87%, Recall: 98.15%, F1: 95.94%).
  • Demonstrated that tracking a subset of birds (80% in week 1, 60% in week 4, 40% in week 7) sufficiently represents group activity (r ≥ 0.90; p ≤ 0.048).
  • The platform requires no additional training or annotation for animal tracking.

Conclusions:

  • The developed platform provides a practical and accessible solution for animal activity tracking.
  • It significantly enhances animal behavior analytics with minimal user effort.
  • This open-source tool democratizes advanced animal welfare monitoring for researchers.