An on-line visual human tracking algorithm using SURF-based dynamic object model

The interest point based tracking methods suffer from the limitation of unavailability of sufficient number of matching key points for the target in all frames of a running video. In this paper, a dynamic model is proposed for describing the object model which is used for tracking a human in a non-stationary video. This dynamic model takes into account the change in the pose as well as the motion of the human. A simple autoregression based predictor is used for dealing with the case of full occlusion. Simulation results are provided to show the efficacy of the algorithm.