Occlusion problem

The occlusion problem is similar to the problem of incomplete foreground extraction, which is hard to solve in our hypothesis. The training process generates a category classifier which represents the characteristic and structure to the class of posture. And for those silhouettes which suffer occlusion problem, the characteristic and structure change, so that they will be recognized as different kind of class. To solve it, we may use the experience of learning-based face detection approach. It solves the occlusion problem by building another data base which represent the class of incomplete face, and recognize them as a face. For example, for an occulted face which failed in the detection of a complete face detector, we perform another incomplete face detector to classify it, so that it may be detected. Along this

vein, we may construct an incomplete data base for each posture to generate an incomplete posture classifier so that we solve the problem. But, it will turn our problem more complicated.

6. Conclusion

In this thesis, we have constructed a real-time human posture recognizing system. We have adapted the AdaBoost training approach and modified the feature with edge gradient orientation feature rather than Harr rectangle feature. Depending on the significant discriminating ability of our feature, the system recognized the silhouette information more efficient and more effective than the traditional AdaBoost approach. Additionally, our approach could directly construct a lot of distinguishing features from significant body structure rather than training all possible features, and thus extremely reduced the training time. From the experiment results, we have showed the efficiency and accuracy of our system.

Although there still remained some difficulties, we have constructed practical system for real-time human posture recognition.

References

[1] Ismail Haritaoglu; David Harwood; Larry S. Davids; ” W : real-time surveillance of people and their activities

4

”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 22, No. 8, August 2000.

[2] Junji Yamato; Jun Ohya; Kenichiro Ishii; “Recognizing human action in time-sequential images using hidden Markov model ”. IEEE Computer Society Conference on

Computer Vision and Pattern Recognition, Jun 1992.

[3] Aphrodite Galata; Neil Johnson; David Hogg; “Learning structured behaviour models using variable length Markov models ”. IEEE International Workshop, pages 15-102, September 1999.

[4] T. Moeslund and E. Granum, “A Survey of Computer Vision-based Human Motion Capture,” Computer Vision and Image Understanding, Vol.81, no.3, pp.231-268, Mar.

2001.

[5] B. Boulay, F. Bremond, and M. Thonnat, “Human Posture Recognition in Video

Sequence,” in Proc. IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 23-29, 2003.

[6] Paul Viola; Michael J. Jones; “Robust Real-Time Face Detection ”. IEEE International Conference on Computer Vision, 2001.

[7] Geogory Shakhnarovich; Paul Viola; Trevor Darrell; “Fast Pose Estimation with

Parameter-Sensitive Hashing ”. Proceedings of Ninth IEEE International Conference on Computer Vision, 2003.

[8] Liu Ren; Gregory Shakhnarovich; Jessica K. Hodgins; Hanspeter Pfister; Paul Viola;

“Learning Silhouette Features for Control of Human Motion ”. ACM Transactions on Graphics, Vol.24(4), October, 2005.

[9] J. K. Aggarwal; Q. Cai; “Human Motion Analysis : A Review ”. IEEE Proceedings on

Nonrigid and Articulated Motion Workshop , pages:90 – 102. June 1997.

[10] J. K. Aggarwal; Q. Cai; W. Liao; B. Sabata; “Articulated and elastic non-rigid motion: a review ”. IEEE Workshop on Motion of Non-Rigid and Articulated Objects , pages:2 – 14, Nov. 1994.

[11] Z. Chen; H. J. Lee; “Knowledge-guided visual perception of 3D human gait from a single image sequence ”. IEEE Transactions on Systems, Man, and Cybernetics,22(2), pages:336-342, 1992.

[12] M. K. Leung; Y. H. Yang; “First sight: A human body outline labeling system “. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 359-377, 1995.

[13] D. Hogg. “Model-based vision: a program to see a walking person ”. Image and Vision Computing, pages 5-20, 1983.

[14] R. F. Rashid. “Towards a system for the interpretation of moving light display ”. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 574-581, November 1980.

[15] A. F. Bobick; J.Davids; “Real-time recognition of activity using temporal

templates ”.IEEE Computer Society Workshop Applications on Computer Vision, pages 39-42, 1996.

[16] R. Polana; R.Nelson; “Low level recognition of human motion (or how to get your man without finding his body parys) ”. IEEE Computer Society Workshop on Motion of Non-Rigid and Articulated Objects, pages 77-82, 1994.

[17] Aphrodite Galata; Neil Johnson; David Hogg; “Learning Variable-Length Markov Models of Behavior”. IEEE Computer Vision and Image Understanding, pages 398-413, 2001.

[18] Junghye Min; Rangachar Kasturi; “Activity Recognition Based on Multiple Motion Trajectories”. IEEE International Conference on Pattern Recognition, 2004.

Using Shape Contexts”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002.

[20] Greg Mori; Jitendra Malik; “Estimating Human Body Configurations Using Shape Context Matching”. European Conference on Computer Vision, pages 666-680, 2002.

[21] Jamie Shotton; Andrew Blake; Reberto Cipolla; “Contour-Based Learning for Object Detection ”. IEEE International Conference on Computer Vision, pages 503-510, 2005.

[22] SJ Wang; LC Kuo; HH Jong; ZH Wu;” Representing images using points on image surfaces”. IEEE Transactions on Image Processing, VOL. 14, NO. 8, AUGUST 2005.

[23] Chong-Wah Ngo; Ting-Chuen Pong; Hong-Jiang Zhang; “Motion Analysis and

Segmentation Through Spatio-Temporal Slices Processing ”.IEEE Transactions on Image Processing, Vol. 12, NO 3, March 2003.

[24] David. G Lowe; ” Object recognition from local scale-invariant features ”, IEEE International Conference on Computer Vision, pages: 1150-1157, 1999.

[25] Correa, P.; Czyz, J.; Umeda, T.; Marques, F.; Marichal, X.; Macq, B.; “Silhouette-based probabilistic 2D human motion estimation for real-time applications”, IEEE International Conference on Image Processing, pages 836-839 2005.

[26] I. C. Chang and C. L. Huang, “The Model-based Human Body Motion Analysis System,”

Image Vision Computation, Vol.18, no.14, pp.1067-1083, 2000.