AlphaChimp: Tracking and Behavior Recognition of Chimpanzees
Xiaoxuan Ma, Yutang Lin, Yuan Xu, Stephan P. Kaufhold, Jack, Terwilliger, Andres Meza, Yixin Zhu, Federico Rossano, Yizhou Wang

TL;DR
AlphaChimp is an innovative end-to-end computer vision method that significantly improves tracking and behavior recognition of chimpanzees in videos, especially for complex social interactions, advancing primatology and AI research.
Contribution
It introduces AlphaChimp, a novel architecture combining temporal feature fusion and Transformer-based self-attention for superior primate behavior analysis.
Findings
10% higher tracking accuracy than existing methods
20% improvement in behavior recognition accuracy
Excels in recognizing complex social behaviors
Abstract
Understanding non-human primate behavior is crucial for improving animal welfare, modeling social behavior, and gaining insights into both distinctly human and shared behaviors. Despite recent advances in computer vision, automated analysis of primate behavior remains challenging due to the complexity of their social interactions and the lack of specialized algorithms. Existing methods often struggle with the nuanced behaviors and frequent occlusions characteristic of primate social dynamics. This study aims to develop an effective method for automated detection, tracking, and recognition of chimpanzee behaviors in video footage. Here we show that our proposed method, AlphaChimp, an end-to-end approach that simultaneously detects chimpanzee positions and estimates behavior categories from videos, significantly outperforms existing methods in behavior recognition. AlphaChimp achieves…
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Taxonomy
TopicsPrimate Behavior and Ecology · Neural dynamics and brain function · Memory and Neural Mechanisms
