Segmentation of human body parts using deformable triangulation

Jun-Wei Hsieh*, Chi Hung Chuang, Sin Yu Chen, Chih Chiang Chen, Kuo Chin Fan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

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 model-driven 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.

Original languageEnglish
Article number5424021
Pages (from-to)596-610
Number of pages15
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Volume40
Issue number3
DOIs
StatePublished - 1 May 2010

Keywords

  • Abnormal event detection
  • Behavior analysis
  • Body part segmentation
  • Video surveillance

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