Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment

• Main Authors: Huang, Hsiang-Wei, Yang, Cheng-Yen, Jiang, Zhongyu, Kim, Pyong-Kun, Lee, Kyoungoh, Kim, Kwangju, Ramkumar, Samartha, Mullapudi, Chaitanya, Jang, In-Su, Huang, Chung-I, Hwang, Jenq-Neng
• Format: Journal Article
• Language: English
• Published: 19-04-2023
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2304.09471

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.