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Fast-Poly: A Fast Polyhedral Framework For 3D Multi-Object Tracking

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arxiv 2403.13443 v2 pith:ZPDRYBYC submitted 2024-03-20 cs.CV cs.RO

Xiaoyu Li , Dedong Liu , Yitao Wu , Xian Wu , Lijun Zhao , Jinghan Gao This is my paper · ORCID
classification cs.CVcs.RO
keywords fast-polytrackingaccuracydatasetfastinferencemulti-objectspeed
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3D Multi-Object Tracking (MOT) captures stable and comprehensive motion states of surrounding obstacles, essential for robotic perception. However, current 3D trackers face issues with accuracy and latency consistency. In this paper, we propose Fast-Poly, a fast and effective filter-based method for 3D MOT. Building upon our previous work Poly-MOT, Fast-Poly addresses object rotational anisotropy in 3D space, enhances local computation densification, and leverages parallelization technique, improving inference speed and precision. Fast-Poly is extensively tested on two large-scale tracking benchmarks with Python implementation. On the nuScenes dataset, Fast-Poly achieves new state-of-the-art performance with 75.8% AMOTA among all methods and can run at 34.2 FPS on a personal CPU. On the Waymo dataset, Fast-Poly exhibits competitive accuracy with 63.6% MOTA and impressive inference speed (35.5 FPS). The source code is publicly available at https://github.com/lixiaoyu2000/FastPoly.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A data-driven Kalman filter with learned transition residuals and gains achieves near-state-of-the-art 3D multi-object tracking on KITTI at real-time speed.

  2. Cross-View Referring Multi-Object Tracking

    cs.CV 2024-12 conditional novelty 6.0 of 10

    The authors propose the CRMOT task, the CRTrack benchmark of 13 scenes and 221 language descriptions, and the CRTracker method, which combines CrossMOT-style tracking with APTM text-image matching.

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