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

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 7 the number of processed frames for handover, with 2 lower latency while providing 1.5 tracking precision improvement compared to the state-of-The-Art.