Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment
This improves indoor people tracking for applications like retail and healthcare, but it is incremental as it builds on existing tracking methods.
The paper tackled multi-camera people tracking by proposing a method using anchor-guided clustering and spatio-temporal consistency for cross-camera re-identification and ID reassignment, achieving an IDF1 of 95.36% and first place in the CVPR AI City Challenge 2023.
Multi-camera multiple people tracking has become an increasingly important area of research due to the growing demand for accurate and efficient indoor people tracking systems, particularly in settings such as retail, healthcare centers, and transit hubs. We proposed a novel multi-camera multiple people tracking method that uses anchor-guided clustering for cross-camera re-identification and spatio-temporal consistency for geometry-based cross-camera ID reassigning. Our approach aims to improve the accuracy of tracking by identifying key features that are unique to every individual and utilizing the overlap of views between cameras to predict accurate trajectories without needing the actual camera parameters. The method has demonstrated robustness and effectiveness in handling both synthetic and real-world data. The proposed method is evaluated on CVPR AI City Challenge 2023 dataset, achieving IDF1 of 95.36% with the first-place ranking in the challenge. The code is available at: https://github.com/ipl-uw/AIC23_Track1_UWIPL_ETRI.