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Enhanced Human Parsing with Multiple Feature Fusion and Augmented Pose Model
Zhaoxiang Zhang; Jianliang Hao; Yunhong Wang; Yuhang Zhao
2014-08-24
会议名称International Conference on Pattern Recognition
会议录名称ICPR 2014
会议日期24-28 August 2014
会议地点Stockholm, Sweden
摘要We address the problem of human pose estimation, which is a very challenging problem due to view angle variance, noise and occlusions. In this paper, we propose a novel human parsing method which can estimate diverse human poses from real world images. We merge the parallel lines feature and uniform LBP feature, thereby the new feature contains both shape and texture information, which can be used by discriminative body part detectors. The standard tree model is augmented by using virtual nodes in order to describe the correlations between originally unconnected nodes, which enhances the robustness of the traditional kinematic tree model. We test our method in a sports image dataset, and the experimental results demonstrate the advantages of the merged feature as well as the augmented pose model in real applications.
关键词Estimation Kinematics Image Edge Detection Heuristic Algorithms Feature Extraction Biological System Modeling Inference Algorithms
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13239
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
Zhaoxiang Zhang,Jianliang Hao,Yunhong Wang,et al. Enhanced Human Parsing with Multiple Feature Fusion and Augmented Pose Model[C],2014.
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