MobiTrack: Mobile Crowdsensing-Based Object Tracking with Min-Region and Max-Utility

Exploiting mobile cameras embedded on the widely-used smartphones to serve object tracking offers a new dimension to reduce the deployment cost of the stationary cameras and shorten the tracking latency, but brings the challenges in efficient task assignment and cooperations among workers due to the requirement of Mobile Crowdsensing (MCS) system. Most existing effort in the literature focuses on object tracking with MCS where the workers capture the moving object photos at pre-calculated sites. However, the contradiction between the tracking coverage and the system cost in these MCS-based tracking solutions is sharpened when tracking scenarios and worker number vary. In this paper, we investigate the tracking region to conduct the task assignment among top-k most probable sensing locations, which can achieve maximal tracking utility. Specifically, we construct a N-Gram prediction model to determine the k tracking locations and formulate the task assignment problem solved by the Kuhn-Munkras algorithm, respectively, laying a theoretical foundation. The prediction model soundness is verified statistically and the task assignment effectiveness is evaluated via large scale real-world data simulations.