Vision and RFID-based person tracking in crowds from a mobile robot

This paper deals with the tracking of persons in a human cluttered environment. It is performed by an active perception system, consisting of a camera mounted on a pan-tilt unit and a 360° RFID detection system which are embedded on a mobile robot. Particle filters enable the fusion of heterogeneous data into the proposal distribution from which the particles are sampled. The information provided by the tracker is then used to build sensor-based dedicated control laws in order to make the robot follow the RFID tagged person. Finally, experiments on our mobile robot are presented in order to highlight the relevance and complementarity of the developed perceptual functions.

I. Introduction and framework

The ability for a mobile robot to automatically follow a person in public areas is a key issue to effectively interact with the surrounding world. To fulfill this objective, robust algorithms able to track a given person thanks to multiple on-boarded sensors are required. Particle filters [2] are currently investigated for person tracking in both robotics and vision communities. Their popularity stems from their ability to fuse in a probabilistic way various kinds of visual measurements. Pérez et al. in [10] highlight the fact that intermittent cues are suitable candidates for the construction of detection modules and efficient proposal distributions. Clearly, reliable people detectors improve the tracking performance.