Efficient Online Multi-Camera Tracking with Memory-Efficient Accumulated Appearance Features and Trajectory Validation

Lap Quoc Tran, Huan Duc Vi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7217-7226

Multi-camera tracking (MCT) plays a crucial role in various computer vision applications. However accurate tracking of individuals across multiple cameras faces challenges particularly with identity switches. In this paper we present an efficient online MCT system that tackles these challenges through online processing. Our system leverages memory-efficient accumulated appearance features to provide stable representations of individuals across cameras and time. By incorporating trajectory validation using hierarchical agglomerative clustering (HAC) in overlapping regions ID transfers are identified and rectified. Evaluation on the 2024 AI City Challenge Track 1 dataset demonstrates the competitive performance of our system achieving accurate tracking in both overlapping and non-overlapping camera networks. With a 40.3% HOTA score our system ranked 9th in the challenge. The integration of trajectory validation enhances performance by 8% over the baseline and the accumulated appearance features further contribute to a 17% improvement.