mlsanityData to 14 Oct 2026

Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration

arXiv 2105.14683 ↗First posted 31 May 2021cs.CVcited by 8

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Abstract (arXiv)

In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation. Most existing MOT methods track multiple objects using a singular RGB camera, which are prone to camera field-of-view and suffer tracking failures in complex scenarios due to background clutters and poor light conditions. To meet these challenges, we propose a MultiModality PAnoramic multi-object Tracking framework (MMPAT), which takes both 2D panorama images and 3D point clouds as input and then infers target trajectories using the multimodality data. The proposed method contains four major modules, a panorama image detection module, a multimodality data fusion module, a data association module and a trajectory inference model. We evaluate the proposed method on the JRDB dataset, where the MMPAT achieves the top performance in both the detection and tracking tasks and significantly outperforms state-of-the-art methods by a large margin (15.7 and 8.5 improvement in terms of AP and MOTA, respectively).

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 63 of its 92 references that are in the map. First posted on arXiv 31 May 2021.

Builds on

63 of 92 references.

References · 63

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38 more
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50 most cited of 63 All 63 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 2; 2025: 2; 2026: 1.

8 papers.