A novel SURF-based algorithm for tracking a 'Human' in a dynamic environment

Detecting and tracking a human from a mobile robot platform has several applications in service robotics where a robot is expected to assist humans. In this paper, we propose a novel interest point-based algorithm that can track a human reliably under several challenging situations like variation in illumination, pose change, scaling, camera motion and occlusion. The limitations of point-based methods are overcome using colour information and imposing a structure on the colour blobs. Whenever sufficient number of SURF matching points are not available for a given frame, the presence of human is detected using Markov random field based graph matching algorithm. Imposition of structure on coloured blobs helps in eliminating background objects having similar colour distribution. The stability-versus-plasticity dilemma inherent in tracking over long run is resolved by selecting new templates on-line and maintaining a tree of templates which is updated with new information. The performance of the algorithm is demonstrated through simulation on standard datasets and the computation time is found to be comparable with existing SURF-based tracking methods.