Online Multi-camera People Tracking with Spatial-temporal Mechanism and Anchor-feature Hierarchical Clustering

Riu Cherdchusakulchai, Sasin Phimsiri, Visarut Trairattanapa, Suchat Tungjitnob, Wasu Kudisthalert, Pornprom Kiawjak, Ek Thamwiwatthana, Phawat Borisuitsawat, Teepakorn Tosawadi, Pakcheera Choppradi, Kasisdis Mahakijdechachai, Supawit Vatathanavaro, Worawit Saetan, Vasin Suttichaya; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7198-7207

Multi-camera Multi-object tracking (MTMC) surpasses conventional single-camera tracking by enabling seamless object tracking across multiple camera views. This capability is critical for security systems and improving situational awareness in various environments. This paper proposes a novel MTMC framework designed for online operation. The framework employs a three-stage pipeline: Multi-object Tracking (MOT) Multi-target Multi-camera Tracking (MTMC) and Cross Interval Synchronization (CIS). In the MOT stage ReID features are extracted and localized tracklets are created. MTMC links these tracklets across cameras using spatial-temporal constraints and constraint hierarchical clustering with anchor features for improved inter-camera association. Finally CIS ensures the temporal coherence of tracklets across time intervals. The proposed framework achieves robust tracking performance validated on the challenging 2024 AI City Challenge with a HOTA score of 51.0556% ranking sixth. The code is available at: https://github.com/AI-and-Robotics-Ventures/AIC2024_Track1_ARV