Part-Segment Features with Optimized Shape Priors for Articulated Pose Estimation

Abstract

We propose part-segment (PS) features for estimating an articulated pose in still images. The PS feature evaluates the image likelihood of each body part (e.g. head, torso, and arms) robustly to background clutter and nuisance textures on the body. While general gradient features (e.g. HOG) might include many nuisance responses, the PS feature represents only the region of the body part by iterative segmentation while updating the shape prior of each part. In contrast to similar segmentation features, part segmentation is improved by part-specific shape priors that are optimized by training images with fully-automatically obtained seeds. The shape priors are modeled efficiently based on clustering for fast extraction of PS features. The PS feature is fused complementarily with gradient features using discriminative training and adaptive weighting for robust and accurate evaluation of part similarity. Comparative experiments with public datasets demonstrate improvement in pose estimation by the PS features.