Overlap Suppression Clustering for Offline Multi-Camera People Tracking
Ryuto Yoshida, Junichi Okubo, Junichiro Fujii, Masazumi Amakata, Takayoshi Yamashita
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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.