People Tracking on a Mobile Companion Robot

Abstract

Developing methods for people tracking on mobile robots is of great interest to engineers and scientists alike. Plenty of research is focused on pedestrian tracking in public areas. Yet, fewer work exists on practical people tracking in home environments with non-static cameras. This paper presents a real time people tracking system for mobile robots that filters asynchronous, multi-modal detections using a Kalman filter for each person. It allows for upright and sitting pose people tracking in home environments. We evaluate the performance of the tracking system using different detection modalities and compared it to state-of-the-art people detection methods. Evaluation was done on a newly collected indoor data set which we made publicly available for comparison and benchmarking.