Human Motion Modeling Using Multivision

In this paper, we propose a gesture modeling system based on computer vision in order to recognize a gesture naturally without any trouble between a system and a user using real-time 3D modeling information on multiple objects. It recognizes a gesture after 3D modeling and analyzing the information pertaining to the user's body shape in stereo views for human movement. In the 3D-modeling step, 2D information is extracted from each view by using an adaptive color difference detector. Potential objects such as faces, hands, and feet are labeled by using the information from 2D detection. We identify reliable objects by comparing the similarities of the potential objects that are obtained from both the views. We acquire information on 2D tracking from the selected objects by using the Kalman filter and reconstruct it as a 3D gesture. A joint of each part of a body is generated in the combined objects. We experimented on ambiguities using occlusion, clutter, and irregular 3D gestures to analyze the efficiency of the proposed system. In this experiment, the proposed gesture modeling system showed a good detection and a processing time of 30 frames per second, which can be used in a real-time.

[1]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[2]  James W. Davis.  Hierarchical motion history images for recognizing human motion , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[3]  Takeo Kanade,et al.  Appearance-based virtual view generation from multicamera videos captured in the 3-D room , 2003, IEEE Trans. Multim..

[4]  Mubarak Shah,et al.  Monitoring human behavior from video taken in an office environment , 2001, Image Vis. Comput..

[5]  Takeo Kanade,et al.  Appearance-based virtual view generation of temporally-varying events from multi-camera images in the 3D room , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[6]  Yi Li,et al.  A relaxation algorithm for real-time multiple view 3D-tracking , 2002, Image Vis. Comput..

[7]  R. Chellappa,et al.  Markerless Motion Capture using Multiple Cameras , 2005, Computer Vision for Interactive and Intelligent Environment (CVIIE'05).

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

[9]  Katsu Yamane,et al.  High Marker Density Motion Capture by Retroreflective Mesh Suit , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

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

[11]  S. Yabukami,et al.  Development of real-time and highly accurate wireless motion capture system utilizing soft magnetic core , 2005, IEEE Transactions on Magnetics.

[12]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  Thomas B. Moeslund,et al.  Multiple cues used in model-based human motion capture , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[15]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[16]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[17]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Luis Rueda,et al.  Advances in Image and Video Technology, Second Pacific Rim Symposium, PSIVT 2007, Santiago, Chile, December 17-19, 2007, Proceedings , 2007, PSIVT.

[19]  Takeo Kanade,et al.  Shape-From-Silhouette Across Time Part II: Applications to Human Modeling and Markerless Motion Tracking , 2005, International Journal of Computer Vision.

[20]  Jong-Ho Kim,et al.  Effective Face Detection Using a Small Quantity of Training Data , 2006, PSIVT.

[21]  Francis Schmitt,et al.  Silhouette and stereo fusion for 3D object modeling , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[22]  R. Venkatesh Babu,et al.  Recognition of human actions using motion history information extracted from the compressed video , 2004, Image Vis. Comput..

[23]  H. Kikuchi,et al.  Motion capture system of magnetic markers using three-axial magnetic field sensor , 2000 .

[24]  Rin-ichiro Taniguchi,et al.  Performance evaluation of vision-based real-time motion capture , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[25]  Maja J. Mataric,et al.  Motion capture from inertial sensing for untethered humanoid teleoperation , 2004, 4th IEEE/RAS International Conference on Humanoid Robots, 2004..