  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/.
