Iterative Human Pose Estimation based on A New Part Appearance Model

Human pose estimation has become a hot topic in the field of computer vision , it can be used in human activity analysis, video, and any other field ,it main purpose is that detect the position, scale and direction of parts of people .Because of the result in the iterative human pose estimation based on tree-structure model is susceptible to the background, In this paper(consider static images),part appearance model is improved, an appearance model based on colour and texture for iterative human pose estimation is proposed, experimental results show this method give a better performance and accuracy while reduce the search space by people detector and grab cut.

[1]  Shihong Lao,et al.  Part Detector Based Human Pose Estimation in Images and Videos: Part Detector Based Human Pose Estimation in Images and Videos , 2011 .

[2]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  Ramakant Nevatia,et al.  Multiple pose context trees for estimating human pose in object context , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[4]  Vittorio Ferrari,et al.  Better Appearance Models for Pictorial Structures , 2009, BMVC.

[5]  Stan Sclaroff,et al.  3D hand pose reconstruction using specialized mappings , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  Ankur Agarwal,et al.  3D human pose from silhouettes by relevance vector regression , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[7]  Deva Ramanan,et al.  Learning to parse images of articulated bodies , 2006, NIPS.

[8]  Zhou Jie.  Human pose estimation in static images based on region segmentation and Monte Carlo sampling , 2011 .

[9]  Cristian Sminchisescu,et al.  Training Deformable Models for Localization , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  Mark Everingham,et al.  Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation , 2010, BMVC.

[11]  Yi Yang,et al.  Articulated pose estimation with flexible mixtures-of-parts , 2011, CVPR 2011.

[12]  Jitendra Malik,et al.  Recognizing action at a distance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[13]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

[14]  Larry S. Davis,et al.  Human detection using partial least squares analysis , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  In-Ho Choi,et al.  Robust Facial Expression Recognition Using a Smartphone Working against Illumination Variation , 2012 .

[16]  Peng Wei.  Research and Implementation of Human Detection Based on Extended Histograms of Oriented Gradients , 2011 .

[17]  Nicholas R. Howe,et al.  Silhouette Lookup for Automatic Pose Tracking , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[18]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[19]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[20]  Dima Damen,et al.  Recognizing linked events: Searching the space of feasible explanations , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Rómer Rosales,et al.  3D Hand Pose Reconstruction Using Specialized Mappings , 2001, ICCV.

[22]  Andrew Zisserman,et al.  Progressive search space reduction for human pose estimation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.