PreActo: Efficient Cross-Camera Object Tracking System in Video Analytics Edge Computing

Thanh-Tung Nguyen, Si Young Jang, Boyan Kostadinov, Dongman Lee

Abstract: Cross-camera real-time object tracking is one of the important, yet challenging applications of video analytics in edge computing environments. To provide accurate and efficient real-time tracking, a tracking target's future movements need to be predicted. Particularly, the destination camera and travel time of the target object are to be identified so that tracking duties can be handover-ed seamlessly. In this paper, we propose a collaborative cross-camera tracking system, called PreActo, with two key features: (1) ResNet-based trajectory learning to exploit the rich spatio-temporal information embedded within objects' moving patterns, which has not been utilized by the existing literature, and (2) collaboration between the edge server and the edge device for real-time trajectory prediction and tracking handover. To prove the validity of our proposed system, we evaluate PreActo on a video dataset leveraging real-world trajectories. Evaluation results show that the proposed system reduces up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7\times$</tex> the number of processed frames for handover, with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\times$</tex> lower latency while providing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.5\times$</tex> tracking precision improvement compared to the state-of-the-art.