Simple Human Gesture Detection and Recognition Using a Feature Vector and a Real-Time Histogram Based Algorithm

DOI: 10.4236/jsip.2011.24040   PDF   HTML     5,294 Downloads   9,191 Views   Citations


Gesture and action recognition for video surveillance is an active field of computer vision. Nowadays, there are several techniques that attempt to address this problem by 3D mapping with a high computational cost. This paper describes software algorithms that can detect the persons in the scene and analyze different actions and gestures in real time. The motivation of this paper is to create a system for thetele-assistance of elderly, which could be used as early warning monitor for anomalous events like falls or excessively long periods of inactivity. We use a method for foreg-round-background segmentation and create a feature vectorfor discriminating and tracking several people in the scene. Finally, a simple real-time histogram based algorithm is described for discriminating gestures and body positions through a K-Means clustering.

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I. Gómez-Conde, D. Olivieri, X. Vila and S. Orozco-Ochoa, "Simple Human Gesture Detection and Recognition Using a Feature Vector and a Real-Time Histogram Based Algorithm," Journal of Signal and Information Processing, Vol. 2 No. 4, 2011, pp. 279-286. doi: 10.4236/jsip.2011.24040.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Department of Economic United Nations and Social Affairs Population Division, “World Population Ageing 2009,” Technical Report, 2010.
[2] M. Lustrek and B. Kaluza, “Fall Detection and Activity Recognition with Machine Learning,” Informatica (Slovenia), Vol. 33, No. 2, 2009, pp. 197-204.
[3] H. Zhou, Y. Yuan and C. Shi, “Object Tracking Using SIFT Features and Mean Shift,” Computer Vision and Image Understanding, Vol. 113, No. 3, 2009, pp. 345-352.
[4] W. Hu, T. Tan, L. Wang and S. Maybank, “A Survey on Visual Surveillance of Object Motion and Behaviors,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 34, No. 3, 2004, pp. 334-352. doi:10.1109/TSMCC.2004.829274
[5] R. Poppe, “A Survey on Vision-Based Human Action Recognition,” Image and Vision Computing, Vol. 28, No. 6, 2010, pp. 976-990. doi:10.1016/j.imavis.2009.11.014
[6] C. Sandoval, A. Albiol, A. Albiol, V. Naranjo and J. M. Mossi, “Robust Motion Detector for Video Surveillance Applications,” Proceedings of International Conference on Image Processing, Vol. 2, 2003, pp. 379-382.
[7] P. Kaewtrakulpong and R. Bowden, “An Improved Adaptive Background Mixture Model for Realtime Tracking with Shadow Detection,” Proceedings of 2nd European Workshop on Advanced Video Based Surveillance Systems, Computer Vision and Distributed Processing, Kluwer Academic Publishers, September 2001, pp. 1-5.
[8] A. Elgammal, R. Duraiswami, D. Harwood and L. S. Davis, “Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance,” Proceedings of the IEEE, Vol. 90, No. 7, 2002, pp. 1151-1163. doi:10.1109/JPROC.2002.801448
[9] A. Mittal and N. Paragios, “Motion-Based Background Subtraction Using Adaptive Kernel Density Estimation,” Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 2004, pp. 302-309.
[10] E. W. Meeds, D. A. Ross, R. S. Zemel and S. T. Roweis, “Learning Stick-Figure Models Using Nonparametric Bayesian Priors over Trees,” IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, 23-28 June 2008, pp. 1-8.
[11] Z. Wei, D. Bi, S. Gao and J. Xu, “Contour Tracking Based on Online Feature Selection and Dynamic Neighbor Region Fast Level Set,” Fifth International Conference on Image and Graphics, Xi’an, 20-23 September 2009, pp. 238-243.
[12] D. Freedman and T. Zhang, “Active Contours for Tracking Distributions,” IEEE Transactions on Image Processing, Vol. 13, No. 4, 2004, pp. 518-526. doi:10.1109/TIP.2003.821445
[13] R. T. Collins and Y. Liu, “On-Line Selection of Discriminative Tracking Features,” Ninth IEEE International Conference on Computer Vision, Vol. 1, October 2003, pp. 346-352.
[14] A. O. Balan, L. Sigal, and M. J. Black, “A Quantitative Evaluation of Video-Based 3D Person Tracking,” 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, Beijing, October 2005, pp. 349-356. doi:10.1109/VSPETS.2005.1570935
[15] D.A. Forsyth, O. Arikan, L. Ikemoto, J. O’Brien, and D. Ramanan, “Computational Studies of Human Motion: Part 1, Tracking and Motion Synthesis,” Foundations and Trends in Computer Graphics and Vision, Vol. 1, No. 2, 2005, pp. 77-254. doi:10.1561/0600000005
[16] P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient Matching of Pictorial Structures,” IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, 2000, pp. 66-73.
[17] B. Kwolek, “Action Recognition in Meeting Videos Using Head Trajectories and Fuzzy Color Histogram,” Informatica (Slovenia), Vol. 29, No. 3, 2005, pp. 281-289.
[18] P. F. Felzenszwalb, R. B. Girshick, D. McAllester and D. Ramanan, “Object Detection with Discriminatively Trained Part-Based Models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, 2010, pp. 1627-1645. doi:10.1109/TPAMI.2009.167
[19] G. Bradski and A. Kaehler, “Learning OpenCV: Computer Vision with the OpenCV Library,” O’Reilly, Cambridge, 2008.
[20] C. M. Bishop, “Pattern Recognition and Machine Learning,” Springer, Berlin, 2006.

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