arXiv Computer Vision
Sep 22

Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT

This paper conducts a systematic empirical study of multi‑object tracking (MOT) algorithms, focusing on how detection and association components affect overall performance. By evaluating state‑of‑the‑art methods on benchmarks such as MOT16/17/20, SportsMOT, DanceTrack, and CrowdTrack, the authors find that detection quality has a far greater impact than association strategies, and that transformer‑based end‑to‑end models are more robust to detection variations but computationally expensive. The study provides a unified pipeline diagram and practical guidance for researchers and practitioners in selecting and designing MOT systems.

By Linh Van Ma, Juhua Hu, Wei Cheng, Unse Fatima, Moongu Jeon
Hugging Face Trending Papers
3d ago

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

VastMAT is a large‑scale multi‑animal tracking benchmark featuring 2,947 videos, 337 animal categories, and over 3.6 million bounding boxes with 22,883 identity trajectories. It emphasizes high‑quality, expert‑reviewed annotations and introduces Seen‑category and Unseen‑category evaluation protocols, revealing significant challenges in tracking unseen animals. The authors also propose a lightweight Center‑Distance‑Augmented Association module that boosts HOTA scores for existing MOT methods without extra training.