🤖 AI Summary
This work addresses the challenge of maintaining identity consistency in LiDAR-based 3D multi-pedestrian tracking under occlusion or crowded scenarios, where insufficient integration of appearance cues often leads to frequent identity switches. To tackle this, we propose a lightweight projection framework that decouples geometric and appearance modeling and introduces a cascaded matching strategy for multimodal data association. This approach significantly enhances trajectory continuity in occluded settings while operating under low latency constraints. Experimental results demonstrate that naive linear fusion is highly susceptible to visual noise, resulting in degraded performance, whereas our method achieves a favorable balance between accuracy and robustness on the KITTI pedestrian dataset. The findings validate that the proposed lightweight architecture effectively strikes an optimal trade-off between discriminative power and computational efficiency.
📝 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 lightweight projection-based framework to decouple geometric and appearance modeling for mobile robots. A comprehensive analysis of feature extraction architectures is conducted, employing lightweight CNNs and Vision Transformers, and evaluating various multi-modal data association strategies to balance computational latency with robust tracking. Experiments on the Pedestrian class of the KITTI dataset reveal that naive linear fusion, of appearance and motion costs, degrades performance due to visual noise. Conversely, a cascaded matching strategy successfully recovers occluded tracks without compromising overall precision, effectively preventing identity switches to maintain human-robot interaction continuity. We show that lightweight architectures can offer an optimal trade-off between the low latency required for safe navigation and the discriminative power needed for social awareness.