Design and Implementation of a Cross-Camera Suspect Tracking System

Abstract

Surveillance systems are everywhere deployed for criminal prevention and suspect tracking for specific security events. The recognition of the target person cross multiple cameras remains a challenge because of the large spatio-temporal uncertainty, which means the entry and the exit of persons in cameras are unpredictable. Therefore, this study design and implement a software system that help users to continuous and stable track of the same target person across multiple cameras. Firstly, the system leverages YOLO and Correlation filter to track the suspicious in single camera. The features of the target suspect are also collected in this stage. Once the target person leaves the scope of a camera, the camera network topology is used to predict the candidate entrance cameras based on the geographical information and tracking path in the first stage. In the second stage, the person re-identification algorithm is used to calculate the similarity possibility and help to identify the person automatically between cameras. Experimental results on public datasets show our system can accurately recognize the target suspect across cameras with acceptable system performance.