Tracking animals across frames fixes wildlife camera classifier errors

Wildlife classifiers trained on curated datasets often misclassify individual animals in real camera-trap footage, producing inconsistent labels frame-to-frame. This work uses standard multi-object tracking algorithms to link detections across consecutive frames and fuse their softmax probabilities into a single consensus label. Tested on three datasets, the approach consistently outperforms standalone classifiers by 2.0–5.1% weighted F1-score, with no architectural changes to the base model. Intended for ecologists and conservation practitioners using camera-trap networks for biodiversity monitoring.