Tracking Targets in Hyper-scale Cameras using Movement Predication

Hyper-scale surveillance cameras have enabled seamless target (e.g., vehicles, individuals) tracking in urban scenarios, significantly improving everyday safety and emergency response capacity. However, practically tracking multiple targets among cameras would incur prohibitive huge computation costs, even with the acceleration utility of edge computing. This work observes with real-world camera deployment that existing tracking scheduling is unfortunately inefficient due to the redundant and over-activation of cameras. We then propose the design of a hierarchical tracking framework HyMOT that exploits fine-grained target movement prediction for efficient tracking in hyper-scale cameras. At its core, HyMOT builds a probabilistic target movement graph with tempo-spatial correlation knowledge extracted from historical statistics. With the graph, we formulate tracking scheduling as an optimization problem with efficiency-accuracy tradeoff constraints and solve this NP-hard problem with a greedy strategy. Experiments based on the Cityflow and Geolife datasets demonstrate that, compared with two baselines, HyMOT requires significantly less (over 90%) computation workloads to track the same amount of targets with similar accuracy.