Group Tracking for Video Monitoring Systems: A Spatio-Temporal Query Processing Approach

Recently, many video monitoring systems utilize deep learning technologies to recognize locations and trajectories of people in video data. In video monitoring systems, a fast discovery of human groups is an important task for several applications, for example, crime surveillance, contact tracing, and customer behavior analysis. To tackle the demand, we propose a group tracking method. First, we propose a spatial proximity definition and define a novel query type, a group tracking query that considers characteristics of video data. A group tracking query retrieves the groups that travel for more than a certain amount of video frame within a certain distance. We propose an efficient query processing method that exploits the spatio-temporal characteristics of groups. Through extensive experiments using real-world datasets, we verify the efficiency and effectiveness of our query definition and query processing method.