Does Appearance Help? A Systematic Study of Image-Based Re-Identification in Online 3D Multi-Pedestrian Tracking
arXiv:2606.07233v1 Announce Type: cross Abstract: LiDAR-based 3D Multi-Object Tracking (MOT) typically relies solely on geometric information, which is often insufficient to distinguish between targets during prolonged occlusions or in crowded human-populated environments. While integrating RGB-based Re-Identification (ReID) offers a theoretical solution for preserving identity context, existing approaches often rely on computationally expensive parallel detectors that hinder real-time robot responsiveness. This work presents a systematic study of image-based ReID in online 3D MOT, utilizing a
This research addresses a critical limitation in real-time robotic perception, namely the difficulty of consistently identifying objects in complex environments, which is a prerequisite for advanced autonomous systems.
Improving re-identification capabilities in 3D multi-pedestrian tracking will significantly enhance the robustness and reliability of autonomous robots and AI agents operating in human-populated spaces.
The explicit integration of image-based Re-Identification into 3D MOT frameworks, moving beyond solely geometric data, allows for better identity preservation and operational efficiency for robots.
- · Robotics companies
- · AI software developers
- · Logistics and delivery sectors
- · Security and surveillance tech
- · Systems heavily relying on purely geometric tracking
- · Developers neglecting multi-modal perception
More reliable and safer interaction between autonomous robots and humans in public or industrial settings.
Accelerated deployment of AI-powered systems in complex, dynamic environments, reducing operational costs.
Increased public acceptance and trust in autonomous systems due to improved predictability and safety features.
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