Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration
This addresses tracking failures in complex scenarios like poor lighting and clutter for autonomous systems, representing a strong specific gain rather than a foundational advance.
The paper tackles multi-object tracking for autonomous driving and robot navigation by proposing a multimodal framework (MMPAT) that uses 2D panorama images and 3D point clouds, achieving top performance on the JRDB dataset with improvements of 15.7 AP and 8.5 MOTA over state-of-the-art methods.
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).