Segmentation of human body parts using deformable triangulation

  • Authors:

  • Jun-Wei Hsieh;Chi-Hung Chuang;Sin-Yu Chen;Chih-Chiang Chen;Kuo-Chin Fan

  • Affiliations:

  • Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan and Department of Electrical Engineering, Yuan Ze University, Chung-Li, Taiwan;Department of Learning and Digital Technology, Fo Guang University, Yilan, Taiwan;Department of Electrical Engineering, Yuan Ze University, Chung-Li, Taiwan;Department of Electrical Engineering, Yuan Ze University, Chung-Li, Taiwan;Department of Learning and Digital Technology, Fo Guang University, Yilan, Taiwan and Department of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
  • Year:
  • 2010

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Abstract

This paper presents a novel segmentation algorithm to segment a body posture into different body parts using the technique of deformable triangulation. To analyze each posture more accurately, they are segmented into triangular meshes, where a spanning tree can be found from the meshes using a depth-first search scheme. Then, we can decompose the tree into different subsegments, where each subsegment can be considered as a limb. Then, two hybrid methods (i.e., the skeleton-based and modeldriven methods) are proposed for segmenting the posture into different body parts according to its occlusion conditions. To analyze occlusion conditions, a novel clustering scheme is proposed to cluster the training samples into a set of key postures. Then, a model space can be used to classify and segment each posture. If the input posture belongs to the nonocclusion category, the skeleton-based method is used to divide it into different body parts that can be refined using a set of Gaussian mixture models (GMMs). For the occlusion case, we propose a model-driven technique to select a good reference model for guiding the process of body part segmentation. However, if two postures' contours are similar, there will be some ambiguity that can lead to failure during the model selection process. Thus, this paper proposes a tree structure that uses a tracking technique so that the best model can be selected not only from the current frame but also from its previous frame. Then, a suitable GMM-based segmentation scheme can be used to finely segment a body posture into the different body parts. The experimental results show that the proposed method for body part segmentation is robust, accurate, and powerful.