Hierarchical pose classification based on human physiology for behaviour analysis

This study presents a new approach to classify human body poses by using angular constraints and variations of body joints. Although different classifications of the poses have been previously made, the proposed approach attempts to create a more comprehensive, accurate and extensible classification by integrating all possible poses based on angles of movement in human joints. The angular variations in all body joints can determine any possible poses. The joint angles from the body axis are computed in the three-dimensional space. In order to train and classify the pose in an automated manner, support vector machines (SVMs) were used. Experiments were carried out on both benchmark (CMU dataset) and in-house simulated (POSER dataset) poses to evaluate the performance of the proposed classification scheme.

[1]  Larry S. Davis,et al.  Human body pose estimation using silhouette shape analysis , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[2]  Mun Wai Lee,et al.  A model-based approach for estimating human 3D poses in static images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[4]  Rita Cucchiara,et al.  Probabilistic posture classification for Human-behavior analysis , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Elaine Nicpon Marieb,et al.  Essentials of Human Anatomy and Physiology , 1981 .

[6]  Shyamsundar Rajaram,et al.  Human Activity Recognition Using Multidimensional Indexing , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[8]  R. Pierce,et al.  Expressive Movement: Posture And Action In Daily Life, Sports, And The Performing Arts , 1992 .

[9]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[10]  Zhaoqin Peng,et al.  Capturing and analyzing of human motion for designing humanoid motion , 2005, 2005 IEEE International Conference on Information Acquisition.

[11]  A. Elgammal,et al.  Inferring 3D body pose from silhouettes using activity manifold learning , 2004, CVPR 2004.

[12]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[13]  Adrian Hilton,et al.  Realistic synthesis of novel human movements from a database of motion capture examples , 2000, Proceedings Workshop on Human Motion.

[14]  Kuntal Sengupta,et al.  Real time detection and recognition of human profiles using inexpensive desktop cameras , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[15]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.