VastMAT: A Large-Scale Multi-Category Benchmark for Multi-Animal Tracking

Zhizhen Li, Zan Wang, Huidong Peng, Bohan Tan, Shimin Shan +2

Multi-animal tracking (MAT) supports the study of animal movement, behavior, and group interactions. However, general multi-object tracking (MOT) benchmarks primarily focus on pedestrians and vehicles, whereas dedicated MAT benchmarks remain limited in jointly supporting broad animal coverage, large-scale video data, and extensive within-video multi-instance association. To address this gap, we introduce VastMAT, which has four key characteristics: (1) Large scale. It comprises 2,947 videos with 1,002,562 annotated frames, totaling 27.85 hours. (2) Broad category coverage. These videos cover 337 animal categories with diverse morphologies and motion patterns. (3) Extensive instance annotations. It provides 3,663,248 bounding boxes and 22,883 identity trajectories---to our knowledge, the largest numbers of both among dedicated MAT benchmarks. (4) High-quality annotations. To ensure reliability, annotations undergo iterative expert review and correction, and quality is assessed through an independent reannotation audit. To systematically assess tracking performance and cross-category generalization, we establish Seen-category and category-disjoint Unseen-category protocols, and…