Adaptive Exemplar-Based Particle Filter for 2D Human Pose Estimation

Abstract

This paper proposes how to utilize pose exemplars in the prediction step of particle filter for efficient human pose estimation. The prediction of particle filter is only dependent on the previous posterior distribution. If observation data and reference dataset are used in prediction, the prediction range can be more compact and precise. We use adaptive exemplars for prediction. To do so the similarity between pose exemplar and the pose silhouette observation are measured. Based on the similarity of exemplars corresponding number of particles are predicted either from exemplars and previous posterior distribution. After pose estimation with the likelihoods of predicted particles, the finally estimated pose is used for updating adaptive exemplar dataset for improving performance of next prediction. Therefore, the proposed method efficiently estimates the pose of articulated full body as resultant images represent.