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Active people recognition using thermal and grey images on a mobile security robot

In this paper we present a vision-based approach to detect, track and identify people on a mobile robot in real time. While most vision systems for tracking people on mobile robots use skin color information, we present an approach using thermal images and a fast contour model together with a particle filter. With this method a person can be detected independently from current light conditions and in situations where no skin color is visible (the person is not close or does not face the robot). Tracking in thermal images is used as an attention system to get an estimate of the position of a person. Based on this estimate we use a pan-tilt camera to zoom to the expected face region and apply a fast face tracker in combination with face recognition to identify the person.