Vision-based human pose estimation for pervasive computing

Vision-based human pose estimation is useful in pervasive computing. In this paper, we proposed an example-based approach to human pose estimation from monocular image sequences. We use human motion capture data to synthesize a pose example database with each pose's 3D information known. Firstly, we use shape context to describe the human silhouette detected from video frames, and get candidates from the pose database by silhouette matching; Secondly, we build probability and statistical model of motion, and carry out pose estimation from these candidates; Finally, Kernel Regression is used to smooth the motion. The proposed method could effectively analyze 3D pose from video and solve the orientation ambiguities problem, also it is invariant to view points. The effectiveness of this method is verified on videos of walking, running and jumping.

[1]  Ronald Poppe,et al.  Vision-based human motion analysis: An overview , 2007, Comput. Vis. Image Underst..

[2]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[3]  Ankur Agarwal,et al.  Recovering 3D human pose from monocular images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Nicholas R. Howe.  Evaluating Lookup-Based Monocular Human Pose Tracking on the HumanEva Test Data , 2006, NIPS 2006.

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

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

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

[8]  Tiziana D'Orazio,et al.  Moving object segmentation by background subtraction and temporal analysis , 2006, Image Vis. Comput..

[9]  Matthew Brand,et al.  Shadow puppetry , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[10]  Ronald Poppe,et al.  Evaluating Example-based Pose Estimation: Experiments on the HumanEva Sets , 2007 .

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

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

[13]  Rómer Rosales,et al.  Estimating 3D body pose using uncalibrated cameras , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[15]  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..

[16]  Nicholas R. Howe,et al.  Silhouette lookup for monocular 3D pose tracking , 2007, Image Vis. Comput..

[17]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[18]  Mannes Poel,et al.  Comparison of silhouette shape descriptors for example-based human pose recovery , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

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

[20]  Michael J. Black,et al.  Learning the Statistics of People in Images and Video , 2003, International Journal of Computer Vision.