AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild
This addresses a gap for researchers in biology, ecology, and animal conservation by providing the first benchmark for MAT, though it is incremental as it adapts existing MOT methods to a new domain.
The authors tackled the scarcity of dedicated benchmarks for multi-animal tracking (MAT) by introducing AnimalTrack, a dataset with 58 sequences across 10 animal categories and 33 target objects per sequence on average, and found that 14 state-of-the-art trackers performed poorly due to differences from pedestrian tracking.
Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is crucial for animal motion and behavior analysis and has many crucial applications such as biology, ecology and animal conservation. Despite its importance, MAT is largely under-explored compared to other MOT problems such as multi-human tracking due to the scarcity of dedicated benchmarks. To address this problem, we introduce AnimalTrack, a dedicated benchmark for multi-animal tracking in the wild. Specifically, AnimalTrack consists of 58 sequences from a diverse selection of 10 common animal categories. On average, each sequence comprises of 33 target objects for tracking. In order to ensure high quality, every frame in AnimalTrack is manually labeled with careful inspection and refinement. To our best knowledge, AnimalTrack is the first benchmark dedicated to multi-animal tracking. In addition, to understand how existing MOT algorithms perform on AnimalTrack and provide baselines for future comparison, we extensively evaluate 14 state-of-the-art representative trackers. The evaluation results demonstrate that, not surprisingly, most of these trackers become degenerated due to the differences between pedestrians and animals in various aspects (e.g., pose, motion, and appearance), and more efforts are desired to improve multi-animal tracking. We hope that AnimalTrack together with evaluation and analysis will foster further progress on multi-animal tracking. The dataset and evaluation as well as our analysis will be made available at https://hengfan2010.github.io/projects/AnimalTrack/.