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About this paper
Mattia Segu, Luigi Piccinelli, Siyuan Li, Yung-Hsu Yang, Bernt Schiele, Luc Van Gool
A new tracking-by-propagation method, SAMBA (Synchronized Set-of-Sequences Modeling for Multiple Object Tracking), leverages selective state-space models and a novel memory synchronization mechanism to capture long-range dependencies and tracklet interactions. It establishes new state-of-the-art performance on challenging benchmarks, including a +3.8 HOTA improvement on DanceTrack and +6.3 HOTA on BFT, enhancing association accuracy in complex, coordinated motion scenarios.
Key insights
- Modeling interactions between tracklets through synchronized memory significantly enhances tracking association accuracy, especially in scenarios with coordinated and complex movements, as evidenced by +1% improvements across metrics from the Sync component.
- Selective State-Space Models (SSMs) provide an efficient, linear-time alternative to transformers for capturing long-range temporal dependencies within tracklets, suitable for indefinitely long video sequences.
- Intelligently masking unreliable observations (MaskObs) during occlusions allows for more robust tracklet propagation by inferring states from past memory and other confidently tracked objects, outperforming simple state freezing by +1.3 HOTA.
Summary by alphaXiv.
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