A framework for human pose estimation by integrating data-driven Markov chain Monte Carlo with multi-objective evolutionary algorithm

In this paper, the problem of human pose estimation is formulated as a multi-objective optimization problem so as to fuse multiple cues more properly, which is in contrast with the hypothesis that the cues are mutually independent so that their consolidation can be solely through the product of their individual likelihood distributions. An evolutionary algorithm for optimizing the defined objectives optimization is applied to evolve a set of non-dominated alternative solutions, known as the Pareto-optimal set. For convergence improvement of the evolutionary algorithm, the DD-MCMC method is used to generate a set of good initial solutions. Evaluating solutions by using relative dominant relation rather than quantitative absolute difference in value makes the solution exploration not dominated by the poor cue and result in more effective solutions for further decision making. Experimental results to the images obtained from different scene are provided to demonstrate the effectiveness and efficiency of our proposed framework