CrowdTracking: Real-Time Vehicle Tracking Through Mobile Crowdsensing

Traditionally, vehicle tracking is accomplished using predeployed video camera networks, which relies on stationary cameras and searches for the target vehicle from videos. In this paper, we develop CrowdTracking, i.e., a crowd tracking system that people can collaboratively keep track of the moving vehicle by taking photographs, especially in areas where video cameras are deficient. In other words, the underlying support of CrowdTracking is mobile crowdsensing. Several novel ideas underpin CrowdTracking. First, the vehicle can be rapidly localized by using both photographing contexts (including the location and the shooting direction) of the photographer and the road network. Second, the moving speed of the vehicle can be estimated according to two localization results and the trajectory will be predicted. As a result, through continuously collecting photographs of the moving vehicle on different roads, the vehicle can be tracked and localized almost in real time. Through precisely localizing the specified vehicle, two optimization objectives are met: 1) maximizing the tracking coverage to the vehicle's actual trajectory and 2) minimizing the number of participants who are assigned vehicle-tracking tasks. We evaluate the localization method with a real dataset and report about 6 m error. We also evaluate the vehicle-tracking method of CrowdTracking using a synthetic data set and experimental results validate its effectiveness and efficiency.