MOT Improves Wildlife Classification from Camera Traps
A study from arXiv (2605.16672) demonstrates that Multi-Object Tracking (MOT) can enhance the consistency of wildlife classification models applied to camera-trap data. While these models achieve high accuracy on curated datasets, their performance degrades under real-world conditions, with labels for the same individual fluctuating across frames. By linking detections through MOT and fusing softmax probabilities over trajectories, the method produces a single consensus label per animal, improving inference reliability.
Key facts
- Camera traps are widely used in ecological research and biodiversity conservation.
- Wildlife classification models are accurate on curated datasets but sensitive to real-world conditions.
- Predictions for a single individual often shift rapidly between frames.
- The study uses Multi-Object Tracking (MOT) to link detections across consecutive frames.
- Trajectories are used to fuse softmax class probabilities.
- The fused probability score yields a single consensus class label.
- The approach exploits the temporal nature of camera-trap data.
- The paper is available on arXiv with ID 2605.16672.
Entities
Institutions
- arXiv