Articulated human pose tracking based on game theory

Abstract

Human pose tracking is among the most popular hotspots in the field of computer vision. In this paper, we propose a novel game theory based method for tracking two dimensional articulated human poses in monocular video sequences. A new probability scheme of game theory is introduced into human pose tracking to find optimal solutions of human poses. The possible limb positions are modeled as strategies of agents who play normal form game with adjacent agents. Likelihood measurements and distance constraints are applied to calculate the payoffs of each of the strategies. Finally, the Nash equilibria are found for each normal form game and the human poses are estimated based on them. In the experiments, the effectiveness and efficiency of the proposed algorithm is fully exhibited.