Privacy preserving multi-target tracking

Abstract

Automated people tracking is important for a wide range of applications. However, typical surveillance cameras are controversial in their use, mainly due to the harsh intrusion of the tracked individuals' privacy. In this paper, we explore a privacy-preserving alternative for multi-target tracking. A network of infrared sensors attached to the ceiling acts as a low-resolution, monochromatic camera in an indoor environment. Using only this low-level information about the presence of a target, we are able to reconstruct entire trajectories of several people. Inspired by the recent success of offline approaches to multi-target tracking, we apply an energy minimization technique to the novel setting of infrared motion sensors. To cope with the very weak data term from the infrared sensor network we track in a continuous state space with soft, implicit data association. Our experimental evaluation on both synthetic and real-world data shows that our principled method clearly outperforms previous techniques.