Vision and RFID data fusion for tracking people in crowds by a mobile robot
In this paper, we address the problem of realizing a human following task in a crowded environment. We consider an active perception system, consisting of a camera mounted on a pan-tilt unit and a 360 360  RFID detection system, both embedded on a mobile robot. To perform such a task, it is necessary to efficiently track humans in crowds. In a first step, we have dealt with this problem using the particle filtering framework because it enables the fusion of heterogeneous data, which improves the tracking robustness. In a second step, we have considered the problem of controlling the robot motion to make the robot follow the person of interest. To this aim, we have designed a multi-sensor-based control strategy based on the tracker outputs and on the RFID data. Finally, we have implemented the tracker and the control strategy on our robot. The obtained experimental results highlight the relevance of the developed perceptual functions. Possible extensions of this work are discussed at the end of the article.
particle filtering radio frequency id multimodal data fusion person tracking person following multi-sensor fusion human visual servoing