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Computer Vision for Detection of Body Posture and Behavior of Red Foxes
Anne K Schütz1, E Tobias Krause2, Mareike Fischer3
1Friedrich-Loeffler-Institut (FLI), Federal Research Institute for Animal Health, Institute of Epidemiology, Südufer 10, 17493 Greifswald-Insel Riems, Germany.
Animals : an Open Access Journal From MDPI
|February 15, 2022
Summary
Computer vision accurately monitors red fox behavior in experiments. This non-invasive method tracks animal activity and posture, ensuring welfare through cost-efficient, real-time evaluation.
Area of Science:
- Animal behavior research
- Computer vision applications
- Animal welfare science
Background:
- Animal behavior is a key indicator of health and welfare, crucial in experimental settings.
- Continuous monitoring of animal behavior is essential for ethical research and welfare assessment.
- Manual analysis of animal behavior from video data is time-consuming and costly.
Purpose of the Study:
- To develop and evaluate a computer vision system for non-invasive monitoring of red fox behavior in an experimental setting.
- To enable cost-efficient and real-time assessment of animal welfare through behavior analysis.
- To quantify animal activity and body posture (lying, sitting, standing) using automated methods.
Main Methods:
- Utilized a neural network-based computer vision algorithm for detecting and tracking red foxes.
- Trained the algorithm to identify specific body postures: 'lying', 'sitting', and 'standing'.
- Employed video monitoring as a non-invasive and cost-efficient data collection tool.
Main Results:
- The computer vision detector achieved a high mean average precision of 99.91% for red fox detection and posture classification.
- The system enabled nearly continuous monitoring of animal behavior by combining activity and posture data.
- The detector demonstrated suitability for real-time evaluation of animal behavior.
Conclusions:
- Computer vision offers a powerful and cost-efficient solution for real-time monitoring of red fox behavior in experimental settings.
- Automated behavior analysis using computer vision significantly improves the efficiency and accuracy of animal welfare assessment.
- This technology supports ethical research practices by facilitating continuous and objective monitoring of animal subjects.
