Detection of Partially Occluded Upper Body Pose Using Hidden Markov Model

Abstract

This paper presents a method that assembles detected human body parts into a upper body human configuration. In the proposed method, six body parts (face, torso, legs and hands) are found by dedicated detectors using support vector machines (SVMs). Next, body configurations are assembled from the detected parts using hidden Markov model (HMM). Utilizing three different HMM with a decision mechanism, partially occluded human upper body poses are successfully assembled. The detection method shows promising results when tested on images from MIT pedestrian database and additional human body pictures.