Generative Estimation of 3D Human Pose Using Shape Contexts Matching

We present a method for 3D pose estimation of human motion in generative framework. For the generalization of application scenario, the observation information we utilized comes from monocular silhouettes. We distill prior information of human motion by performing conventional PCA on single motion capture data sequence. In doing so, the aims for both reducing dimensionality and extracting the prior knowledge of human motion are achieved simultaneously. We adopt the shape contexts descriptor to construct the matching function, by which the validity and the robustness of the matching between image features and synthesized model features can be ensured. To explore the solution space efficiently, we design the Annealed Genetic Algorithm (AGA) and Hierarchical Annealed Genetic Algorithm (HAGA) that searches the optimal solutions effectively by utilizing the characteristics of state space. Results of pose estimation on different motion sequences demonstrate that the novel generative method can achieves viewpoint invariant 3D pose estimation.

[1]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[2]  Cristian Sminchisescu,et al.  Discriminative density propagation for 3D human motion estimation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  Jitendra Malik,et al.  Recovering 3D human body configurations using shape contexts , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Tieniu Tan,et al.  People tracking based on motion model and motion constraints with automatic initialization , 2004, Pattern Recognit..

[5]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[6]  Jake K. Aggarwal,et al.  Human motion analysis: a review , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[7]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[8]  Ian D. Reid,et al.  Automatic partitioning of high dimensional search spaces associated with articulated body motion capture , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[10]  Michael J. Black,et al.  Implicit Probabilistic Models of Human Motion for Synthesis and Tracking , 2002, ECCV.

[11]  Ankur Agarwal,et al.  Tracking Articulated Motion Using a Mixture of Autoregressive Models , 2004, ECCV.

[12]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

[13]  David J. Fleet,et al.  Monocular 3-D Tracking of the Golf Swing , 2005, CVPR.

[14]  Cristian Sminchisescu,et al.  Covariance scaled sampling for monocular 3D body tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Xiaogang Jin,et al.  Convolution surfaces for arcs and quadratic curves with a varying kernel , 2002, The Visual Computer.

[16]  Pascal Fua,et al.  3D Human Body Tracking Using Deterministic Temporal Motion Models , 2004, ECCV.

[17]  Michael J. Black,et al.  HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion , 2006 .

[18]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[19]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..