Human Pose Estimation Using Exemplars and Part Based Refinement

In this paper, we proposed a fast and accurate human pose estimation framework that combines top-down and bottom-up methods. The framework consists of an initialization stage and an iterative searching stage. In the initialization stage, example based method is used to find several initial poses which are used as searching seeds of the next stage. In the iterative searching stage, a larger number of body parts candidates are generated by adding random disturbance to searching seeds. Belief Propagation (BP) algorithm is applied to these candidates to find the best n poses using the information of global graph model and part image likelihood. Then these poses are further used as searching seeds for the next iteration. To model image likelihoods of parts we designed rotation invariant EdgeField features based on which we learnt boosted classifiers to calculate the image likelihoods. Experiment result shows that our framework is both fast and accurate.