SOURCE-LINKED INTELLIGENCE
Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT
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 unifi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T05:57:14.000Z
First collected: 2026-09-24T10:22:25.365Z. This is not the publication date.