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[Speech] Information about Seminar for Master Program (First and second year of the program) )
地點:A棟 演講廳
講題:Model-Based Body Segmentation and Behavior Analysis

This talk presents a novel segmentation algorithm to segment a body posture into different body parts using the technique of deformable triangulation. First of all, to better analyze each posture, we triangulate it into triangular meshes, from which a spanning tree can be found using a depth-first search scheme. Then, two hybrid methods, i.e., the skeleton-based and model-driven ones, are proposed for segmenting the posture into different body parts according to its occlusion conditions. To analyze the occlusion condition, a novel clustering scheme is then proposed for clustering the training samples into a set of key postures. Then, a model space can be formed and used for posture classification and segmentation. After clustering, if the input posture belongs to the non-occlusion category, the skeleton-based scheme will be used for dividing it into different body parts which will be then refined using a set of Gaussian mixture models (GMMs). As to the occlusion case, we propose a model-driven technique for selecting a good reference model for guiding the process of body part segmentation. However, if two postures’ contours are similar, some ambiguity will be caused and lead to the failure in model selection. Thus, this talk proposes a tree structure via a tracking technique for tackling this problem so that the best model can be selected not only from the current frame but also its previous frame. Thus, a suitable GMM-based segmentation scheme can be driven for finely segmenting a body posture into different body parts. Experimental results have proved that the proposed method is robust, accurate, and powerful in body part segmentation.

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