ViTrack: Efficient Tracking on the Edge for Commodity Video Surveillance Systems

Abstract

Nowadays, video surveillance systems are widely deployed in various places, e.g., schools, parks, airports, roads, etc. However, existing video surveillance systems are far from full utilization due to high computation overhead in video processing. In this work, we present ViTrack, a framework for efficient multi-video tracking using computation resource on the edge for commodity video surveillance systems. In the heart of ViTrack lies a two layer spatial/temporal compressive target detection method to significantly reduce the computation overhead by combining videos from multiple cameras. Further, ViTrack derives the video relationship and camera information even in absence of camera location, direction, etc. To address variant video quality and missing targets, ViTrack leverages a Markov Model based approach to efficiently recover missing information and finally derive the complete trajectory. We implement ViTrack on a real deployed video surveillance system with 110 cameras. The experiment results demonstrate that ViTrack can provide efficient trajectory tracking with processing time 45x less than the existing approach. For 110 video cameras, ViTrack can run on a Dell OptiPlex 390 computer to track given targets in almost real time. We believe ViTrack can enable practical video analysis for widely deployed commodity video surveillance systems.