A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset

2025-09-15

AI Summary

To address the limitations of manual behavioral observation in group-housed pigs—including time consumption, subjectivity, poor scalability, and susceptibility to occlusion—this paper proposes a modular computer vision framework integrating zero-shot object detection, motion-aware instance segmentation, identity-preserving multi-object tracking, and Vision Transformer–driven temporal behavior modeling. The framework enables robust, individual-level behavior recognition and continuous monitoring under complex occlusions and dense-group conditions. Evaluated on the Edinburgh Pig Behavior Dataset, it achieves 94.2% behavior classification accuracy—surpassing the state-of-the-art by 21.2 percentage points—along with 93.3% ID preservation rate and 89.3% detection precision. These results significantly advance objective, scalable assessment of animal welfare and health status in agricultural settings.

Technology Category

• Computer Vision: Multi-modal Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings. However, traditional manual observation methods are time-consuming, subjective, and limited in scalability. We present a modular pipeline that leverages open-sourced state-of-the-art computer vision techniques to automate animal behavior analysis in a group housing environment. Our approach combines state-of-the-art models for zero-shot object detection, motion-aware tracking and segmentation, and advanced feature extraction using vision transformers for robust behavior recognition. The pipeline addresses challenges including animal occlusions and group housing scenarios as demonstrated in indoor pig monitoring. We validated our system on the Edinburgh Pig Behavior Video Dataset for multiple behavioral tasks. Our temporal model achieved 94.2% overall accuracy, representing a 21.2 percentage point improvement over existing methods. The pipeline demonstrated robust tracking capabilities with 93.3% identity preservation score and 89.3% object detection precision. The modular design suggests potential for adaptation to other contexts, though further validation across species would be required. The open-source implementation provides a scalable solution for behavior monitoring, contributing to precision pig farming and welfare assessment through automated, objective, and continuous analysis.

Problem

• Automating individual animal behavior analysis in group housing
• Addressing challenges of occlusions and group scenarios
• Providing scalable computer vision for precision farming

Innovation

• Zero-shot object detection for animal tracking
• Motion-aware segmentation for group housing scenarios
• Vision transformers for robust behavior recognition