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

Abstract

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. © 2013 IEEE.