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Pose estimation based on human detection and segmentation 被引量：2

Pose estimation based on human detection and segmentation
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摘要 We address the problem of 3D human pose estimation in a single real scene image. Normally, 3D pose estimation from real image needs background subtraction to extract the appropriate features. We do not make such assumption, In this paper, a two-step approach is proposed, first, instead of applying background subtraction to get the segmentation of human, we combine the segmentation with human detection using an ISM-based detector. Then, silhouette feature can be extracted and 3D pose estimation is solved as a regression problem. RVMs and ridge regression method are applied to solve this problem. The results show the robustness and accuracy of our method. We address the problem of 3D human pose estimation in a single real scene image. Normally, 3D pose estimation from real image needs background subtraction to extract the appropriate features. We do not make such assumption, In this paper, a two-step approach is proposed, first, instead of applying background subtraction to get the segmentation of human, we combine the segmentation with human detection using an ISM-based detector. Then, silhouette feature can be extracted and 3D pose estimation is solved as a regression problem. RVMs and ridge regression method are applied to solve this problem. The results show the robustness and accuracy of our method.
作者 CHEN Qiang ZHENG EnLiang LIU YunCai
机构地区 Institute of image Processing and Pattern Recognition
出处 《Science in China(Series F)》 2009年第2期244-251,共8页 中国科学（F辑英文版）
基金 Supported by the National Basic Research Program of China (Grant No.2006CB303103) Key Program of the National Natural Science Foundation of China (Grant No.60833009)
关键词 human detection and segmentation 3D pose estimation regression machine learning human detection and segmentation 3D pose estimation regression machine learning
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献10

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  • 1杨金锋,傅周宇,谭铁牛,胡卫明.一种新型的基于内容的图像识别与过滤方法[J].通信学报,2004,25(7):93-106. 被引量：27
  • 2EICHNER M,FERRARI V.Better appearance models for pictorial structures[C]//Proceedings of British Machine Vision Conference.London,UK,2009:6-17.
  • 3HU Weiming,WU Ou,CHEN Zhouyao,et al.Recognition of pornographic web pages by classifying texts and images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(6):1019-1034.
  • 4FELZENSZWALB P,HUTTENLOCHER D.Pictorial structures for object recognition[J].International Journal of Computer Vision,2005,61(1):55-79.
  • 5MORI G,REN Xiaofeng,EFROS A,et al.Recovering human body configurations:combining segmentation and recognition[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,DC,USA,2004:326-333.
  • 6REN Xiaofeng,BERG A,MALIK J.Recovering human body configurations using pairwise constraints between parts[C]//IEEE International Conference on Computer Vision.Beijing,China,2005:824-831.
  • 7ROBERTS T,MCKENNA S,RICKETTS I.Human pose estimation using partial configurations and probabilistic regions[J].International Journal of Computer Vision,2007,73(3):285-306.
  • 8LEE M,COHEN I.A model-based approach for estimating human 3D poses in static images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(6):905-16.
  • 9HUA Gang,YANG Minghsuan,WU Ying.Learning to estimate human pose with data driven belief propagation[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Diego,USA,2005:747-754.
  • 10HU Zhilan,WANG Guijin.LIN Xinggang,et al.Recovery of upper body poses in static images based on joints detection[J].Pattern Recognition Letters,2009,30(5):503-12.

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Science in China(Series F)
2009年 第2期

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