Human detection and following by a mobile robot using 3D features

Human-robot interaction is one of the most basic requirements for service robots. In order to provide the desired service, these robots are required to detect and track human beings in the environment. This paper presents a novel approach for classifying a target person in a crowded environment. The system used the approaches for human detection and following by implementing the multi-sensor data fusion technique using stereo camera and laser range finder (LRF). Our system tracks human being by gathering features of human upper body and face in 3D space from stereo camera and uses laser rangefinder to get legs data. Using these data our system classifies the target person from other human beings in the environment. We used Haar cascade classifiers for the detection of upper body and face, and used stereo camera for getting dimensions in 3D space. The approach for gathering legs data is based on the recognition of legs pattern extracted from laser scan. Tracking of target person is done using Cam Shift theorem. Using all these techniques we present a novel approach for target person classification and tracking. Our approach is feasible for mobile robots with an identical device arrangement.