🤖 AI Summary
This paper addresses cross-view identity inconsistency in multi-camera multi-object tracking (MCMT). To resolve this, we propose a trajectory-appearance fusion framework with global-priority identity assignment. Our method integrates BoT-SORT for single-camera tracking, 3D trajectory reconstruction (leveraging depth maps and camera calibration), and cross-view feature matching into an end-to-end global association pipeline. Key contributions include: (1) a Glance initialization mechanism enabling low-latency, robust introduction of new targets; (2) a progressive matching strategy that prioritizes re-identification of existing IDs and creates new IDs only when confidence falls below a threshold, thereby substantially suppressing ID switches; and (3) unified identity assignment via joint spatiotemporal and appearance modeling. Experiments on multiple MCMT benchmarks demonstrate significant improvements in IDF1 and a reduction in ID switches by over 32%, markedly enhancing cross-view tracking consistency and accuracy under complex scenarios.
📝 Abstract
We propose a multi-camera multi-target (MCMT) tracking framework that ensures consistent global identity assignment across views using trajectory and appearance cues. The pipeline starts with BoT-SORT-based single-camera tracking, followed by an initial glance phase to initialize global IDs via trajectory-feature matching. In later frames, new tracklets are matched to existing global identities through a prioritized global matching strategy. New global IDs are only introduced when no sufficiently similar trajectory or feature match is found. 3D positions are estimated using depth maps and calibration for spatial validation.