CrowdTracker: Object tracking using mobile crowd sensing

This paper proposes CrowdTracker, a novel object tracking system based on mobile crowd sensing (MCS). Different from traditional video-based studies, CrowdTracker recruits people to collaboratively take photos of the object to achieve object movement prediction and tracking. The optimization objective of CrowdTracker is to effectively track the moving object in real time and minimize the cost on user incentives. Specifically, the incentive is determined by the number of workers assigned and the total distance that workers move to complete the task. In order to achieve the objective, we propose the MPRE model to predict the object movement and two other algorithms, namely Tcentric and P-centric, for task allocation. Initial experimental results over a large-scale real-world dataset indicate that CrowdTracker can effectively track the object with a low incentive cost.