AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild

TL;DR

AnimalTrack is the first dedicated benchmark dataset for multi-animal tracking in the wild, providing high-quality annotations and baseline evaluations to advance research in this under-explored area.

Contribution

The paper introduces AnimalTrack, a comprehensive benchmark dataset for multi-animal tracking, including annotations, diverse sequences, and baseline evaluations of existing algorithms.

Findings

Existing MOT algorithms perform poorly on animal data.

AnimalTrack reveals challenges due to animal pose, motion, and appearance differences.

Baseline results highlight the need for specialized tracking methods for animals.

Abstract

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/.