Problem
Research questions and friction points this paper is trying to address.
multi-object tracking
detection and association
empirical study
performance evaluation
Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT
🤖 AI Summary
该研究通过系统实验评估了多目标跟踪算法中检测和关联组件的贡献，揭示了检测质量对整体性能的影响远大于关联策略，并提供了设计优化MOT系统的实用指导。
📝 Abstract
This paper presents a comprehensive experimental evaluation and detailed analysis of state-of-the-art multi-object tracking algorithms, with an emphasis on quantifying the individual contributions of detection and association components to overall tracking performance. Unlike existing surveys that primarily offer theoretical categorizations or taxonomies of tracking methods, our work adopts a rigorous experimental perspective grounded in publicly available implementations, providing practical guidance for researchers and practitioners in method selection and system design. We introduce a unified pipeline diagram that consolidates the core components across the two main branches of visual multi-object tracking: tracking-by-detection and end-to-end deep learning paradigms, and systematically analyze the object detection, feature extraction, and data association modules. Through extensive empirical studies on standard benchmarks, including MOT16, MOT17, MOT20, SportsMOT, DanceTrack, and CrowdTrack datasets, we reveal critical insights: (1) detection quality dominates association strategy performance, with detector improvements yielding more than 10% gains compared to less than 5% from refined association strategies; (2) modern deep learning detectors paired with specialized re-identification models significantly outperform joint detection and embedding approaches; and (3) transformer-based end-to-end methods exhibit greater robustness to detection quality variations but at a substantial computational cost. Our findings from extensive experiments provide key insights into component-level effects in MOT, particularly the dominant influence of detection quality relative to association, while offering practical insights for designing and optimizing MOT systems under varying performance and robustness requirements. Code and experimental setups are available at github.com/linh-gist/VisualMOT.
Innovation
Methods, ideas, or system contributions that make the work stand out.
detection quality
association strategy
end-to-end deep learning
transformer-based methods
systematic empirical study
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