Extends online 2D multi-camera tracking to 3D via depth-based point cloud reconstruction, clustering for 3D boxes, and local ID consistency for global data association, placing 3rd on 2025 AI City Challenge 3D MTMC dataset.
Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project
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abstract
Although many methods perform well in single camera tracking, multi-camera tracking remains a challenging problem with less attention. DukeMTMC is a large-scale, well-annotated multi-camera tracking benchmark which makes great progress in this field. This report is dedicated to briefly introduce our method on DukeMTMC and show that simple hierarchical clustering with well-trained person re-identification features can get good results on this dataset.
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cs.CV 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Online 3D Multi-Camera Perception through Robust 2D Tracking and Depth-based Late Aggregation
Extends online 2D multi-camera tracking to 3D via depth-based point cloud reconstruction, clustering for 3D boxes, and local ID consistency for global data association, placing 3rd on 2025 AI City Challenge 3D MTMC dataset.