Overlap Suppression Clustering for Offline Multi-Camera People Tracking

Multi-Camera People Tracking is a multifaceted issue that requires the integration of several computer vision tasks, such as Object Detection, Multiple Object Tracking, and Person Re-identification. This study presents a multi-camera people tracking method that comprises four main processes: (1) single camera people tracking based on overlap suppression clustering, (2) representative image extraction using pose estimation for re-identification, (3) re-identification using hierarchical clustering with average linkage, and (4) low-identifiability tracklets assignment.Our RIIPS team achieved the highest Higher Order Tracking Accuracy (HOTA) of 71.9446% in the 2024 AI City Challenge Track 1.