Human pose estimation using consistent max-covering

A novel consistent max-covering method is proposed for human pose estimation. We focus on problems in which a rough foreground estimation is available. Pose estimation is formulated as a jigsaw puzzle problem in which the body part tiles maximally cover the foreground region, match local image features, and satisfy body plan and color constraints. This method explicitly imposes a global shape constraint on the body part assembly. It anchors multiple body parts simultaneously and introduces hyperedges in the part relation graph, which is essential for detecting complex poses. Using multiple cues in pose estimation, our method is resistant to cluttered foregrounds. We propose an efficient linear method to solve the consistent max-covering problem. A two-stage relaxation finds the solution in polynomial time. Our experiments on a variety of images and videos show that the proposed method is more robust than previous locally constrained methods.

[1]  Daniel P. Huttenlocher,et al.  Efficient matching of pictorial structures , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[2]  Rómer Rosales,et al.  Inferring body pose without tracking body parts , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[3]  Volker Tresp.  The Bayesian , 2001 .

[4]  Jitendra Malik,et al.  Estimating Human Body Configurations Using Shape Context Matching , 2002, ECCV.

[5]  Cordelia Schmid,et al.  Learning to Parse Pictures of People , 2002, ECCV.

[6]  Trevor Darrell,et al.  Fast pose estimation with parameter-sensitive hashing , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Trevor Darrell,et al.  Inferring 3D structure with a statistical image-based shape model , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  Ahmed M. Elgammal,et al.  Inferring 3D body pose from silhouettes using activity manifold learning , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[9]  Mun Wai Lee,et al.  Proposal maps driven MCMC for estimating human body pose in static images , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[10]  David A. Forsyth,et al.  Probabilistic Methods for Finding People , 2001, International Journal of Computer Vision.

[11]  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..

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

[13]  Jitendra Malik,et al.  Recovering human body configurations using pairwise constraints between parts , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  Michael J. Black,et al.  Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[15]  Yuval Rabani,et al.  Linear Programming , 2007, Handbook of Approximation Algorithms and Metaheuristics.

[16]  Wenyu Liu,et al.  Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Dariu Gavrila,et al.  A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  David J. Fleet,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .

[19]  Hao Jiang,et al.  Global pose estimation using non-tree models , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Yihong Gong,et al.  Discriminative learning of visual words for 3D human pose estimation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Mark Everingham,et al.  Combining discriminative appearance and segmentation cues for articulated human pose estimation , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[22]  Bernt Schiele,et al.  Pictorial structures revisited: People detection and articulated pose estimation , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Hao Jiang.  Human pose estimation using consistent max-covering , 2009, ICCV.

[24]  Andrew Zisserman,et al.  Pose search: Retrieving people using their pose , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Ben Taskar,et al.  Adaptive pose priors for pictorial structures , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Stan Sclaroff,et al.  Fast globally optimal 2D human detection with loopy graph models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.