CityTrac: Precise Camera Selection and Movement Prediction for Object Tracking in Hyperscale Public Security Camera Network

Abstract

Using hyperscale surveillance cameras, seamless target tracking can be accomplished in urban security scenarios, significantly enhancing public security and emergency response capabilities. In spite of the advantage of edge computing, tracking multiple targets using multiple cameras would incur prohibitive high computation costs. Based on the deployment of real-world cameras, this research finds that existing tracking scheduling is inefficient as a result of redundant and excessive activation of cameras. As a follow-up, the research proposes a hierarchical tracking framework called CityTrac that leverages fine-grained target movement predictions to provide efficient tracking in hyperscale cameras. First, CityTrac uses a specially designed camera selection strategy that ensures accurate tracking with a minimum number of cameras. After that, CityTrac constructs a probabilistic target movement graph by using historical tempo-spatial correlation information. Using the graph as a model, the tracking scheduling and camera selection problem are formulated as an optimization problem with efficiency-accuracy tradeoff constraints. The research addresses this NP-hard problem using greedy optimization. The experiments conducted with the Cityflow and Geolife datasets demonstrate that, compared with two baselines, CityTrac requires significantly fewer computation resources (over 90%) in order to track the same number of targets with the same level of accuracy.