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MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving

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arxiv 2409.16149 v2 pith:2JS3Q3DT submitted 2024-09-23 cs.CV

Xiyang Wang , Shouzheng Qi , Jieyou Zhao , Hangning Zhou , Siyu Zhang , Guoan Wang , Kai Tu , Songlin Guo
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Jianbo Zhao Jian Li Mu Yang
This is my paper · ORCID
classification cs.CV
keywords mctracktrackingdatasetsmulti-objectacrossgithubhttpsmegvii-research
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This paper introduces MCTrack, a new 3D multi-object tracking method that achieves state-of-the-art (SOTA) performance across KITTI, nuScenes, and Waymo datasets. Addressing the gap in existing tracking paradigms, which often perform well on specific datasets but lack generalizability, MCTrack offers a unified solution. Additionally, we have standardized the format of perceptual results across various datasets, termed BaseVersion, facilitating researchers in the field of multi-object tracking (MOT) to concentrate on the core algorithmic development without the undue burden of data preprocessing. Finally, recognizing the limitations of current evaluation metrics, we propose a novel set that assesses motion information output, such as velocity and acceleration, crucial for downstream tasks. The source codes of the proposed method are available at this link: https://github.com/megvii-research/MCTrack}{https://github.com/megvii-research/MCTrack

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Cited by 3 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. UrbanGS: Semantic-Guided Gaussian Splatting for Urban Scene Reconstruction

    cs.CV 2024-12 conditional novelty 5.0 of 10

    UrbanGS improves urban scene reconstruction by separating static and dynamic Gaussians with 2D semantic maps and adding static-invariance, ground-consistency, and learnable time-embedding losses.

  3. IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter

    cs.CV 2025-02 conditional novelty 4.0 of 10

    A Tracking-by-Detection 3D MOT system using an Interacting Multiple Model filter, damping-window trajectory scoring, and distance-based score reweighting reports 73.8% AMOTA on nuScenes Val, 0.1% above Fast-Poly.

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