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Efficient Human Parsing based on Sketch Representation
Meng Wang; Zhaoxiang Zhang; Yunhong Wang
2012-11-05
会议名称Asian Conference on Computer Vision
会议录名称ACCV 2012
会议日期5-9 November 2012
会议地点Daejeon, Korea
摘要In this paper, we present an efficient human parsing method which estimates human body poses from 2D images. Firstly we propose an edge sketch representation, which enhance critical information for pose estimation and prune the redundant. The sketch representation is generated by employing two sets of filters on extracted edges. Based on sketch representation, body part candidates can be located easily using parallel lines detection in Hough space. Then we use specifically trained linear SVM classifiers to detect each body part candidates based on parallel line feature. A dynamic programming algorithm is applied to calculate the MAP estimation based on standard pictorial structure model, which use a kinematic tree to describe human pose. To evaluate the representing ability of proposed sketch representation, as well as the accuracy and efficiency of our entire human pose estimation method, we run two sets of experiments on a sports image dataset respectively. Experimental results demonstrate that the human body parts in the images can be well described by our proposed sketch representation. Furthermore, our human pose estimation method is efficient and achieves comparable accuracy against the state-of-the-art.
关键词Parsing Representation
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13266
专题类脑智能研究中心
通讯作者Zhaoxiang Zhang
推荐引用方式
GB/T 7714
Meng Wang,Zhaoxiang Zhang,Yunhong Wang. Efficient Human Parsing based on Sketch Representation[C],2012.
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