SAInf: Stay Area Inference of Vehicles using Surveillance Camera Records.

Stay area detection is one of the most important applications in trajectory data mining, which is helpful to understand human's behavior intentions. Traditional stay area detection methods are based on GPS data with relatively high sampling rate. However, because of privacy issues, accessing GPS data can be difficult in most real-world applications. Fortunately, traffic surveillance cameras have been widely deployed in urban area, and it provides us a novel way of acquiring vehicles' trajectories. All the vehicles that traverse by can be recognized and recorded in a passive way. However, the trajectory data collected in this way is extremely coarse, because the surveillance cameras are only deployed in important locations, such as crossroads. This coarse trajectory introduces two challenges for the stay area detection problem, i.e., whether and where the stay event occurs. In this paper, we design a two-stage method to solve the stay area detection problem with coarse trajectories. It first detects the stay event between a surveillance camera record pair, then uses a layer-by-layer stay area identification algorithm to infer the exact stay area. Extensive experiments based on real-world data were used to evaluate the performance of the proposed framework. Results demonstrate the proposed framework SAInf achieved a 58% performance improvement compared with SOTA methods.