Open Access Open Access  Restricted Access Subscription or Fee Access

Dynamic Hierarchial Clustering Based Human Action Classification

K. Menaka, B. Yogameena, P. Karpagavalli


Automatic visual surveillance systems could play an important role in supporting and eventually replacing human observers. The proposed frame work contains four modules viz detection, tracking, feature extraction and action classification. The person has to be found in the image plane first, using robust background subtraction by modifying the parameters in Gaussian Mixture Model (GMM), assuming a static camera. Centroid based tracking is done consecutively. Silhouette shaper based features are extracted, including aspect ratio of minimal bounding box of human silhoutte, approximated eccentricity, normalized central moments. Human actions are classified by grouping, enhancing efficient hierarchial clustering. In this paper a multi-sample-based similarity measure has been proposed where HMM training and distance measuring are based on multiple samples. These multiple training data are acquired by a dynamic hierarchical clustering (DHC) method. By iteratively reclassifying and retraining the data groups at different clustering levels, the initial training and clustering errors due to overfitting will be sequentially corrected in later steps. Human behaviours are obtained from clustered data set and trained neural network kit to describe their appropriate classes. The results are evaluated by means of sensitivity, specificity and accuracy.


Video Surveillance, Background Subtraction, Gaussian Mixture Model, Minimal Bounding Box, Feature Extraction, Hierarchial Clustering, Neural Network

Full Text:



Xinyu Wu, Haitao Gong, Pei Chen, Zhong Zhi and Yangsheng Xu “Intelligent household surveillance robot,” the 2008 IEEE Conference on Robotics and Biometics, Bangkok, Thailand, Feburuary 21-26 , 2009.

M. Piccardi, “Background subtraction techniques:A review,” IEEE International Conference on Systems,Man and Cybernetics, vol.4, pp. 3099-3104, Oct. 2004.

C. Wren, A. Azarbayejani, T. Darrell, and A. ,“Pfinder: Real-Time Tracking of the Human Body,” IEEE Trans.On Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp.780-785, July 1997.

A. Mittal, N. Paragios, “Motion-Based Background Subtraction using Adaptive Kernel Density Estimation,” In Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), pp. 302-309, 2004

J. Hu and T.Su “Robust background subtraction with shadow and Highlight removal for indoor surveillance,” In proceedings of the EURASIP Journal on Advances in signal processing, 14 pages,2007.I. Haritaoglu, D. Harwood, and L. S. Davis, “W4: a real time system for detecting and tracking people,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 962–968, 1998

A. Gilbert, J. Illingworth, and R. Bowden, “Scale invariant action recognition using compound features mined from dense spatio-temporal corners,” in Proceedings on the 10th European Conference on Computer Vision (ECCV ’08), D. Forsyth, P. Torr, and A. Zisserman, Eds., vol. 5302 of Lecturer

Y. Song, L. Goncalves, and P. Perona, “Unsupervised learning of human motion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 7, pp. 814 –827, 2003.s

K. K. C. Lee and Y. Xu, “Modeling human actions from learning,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’04), vol.3, pp. 2787– 2792, October 2004. J. Ben-Arie, Z. Wang, P. Pandit, and S. Rajaram, “Human activity recognition using multidimensional indexing,” IEEE s Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1091–1104, 2002

Fan Jiang, Ying Wu, Aggelos K. Katsaggelos “Abnormal event detection from surveillance video by dynamic hierarchical clustering”, Proceedings of the IEEE Conference ICIP, 2007

J. Ajmera and C. Wooters, “A Robust Speaker Clustering Algorithm,” , IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 411-416, December 2003.

F. Porikli and T. Haga, “Event Detection by Eigenvector Decomposition Using Object and Frame Features”, IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 114-114, June 2004.

D. Zhang, D. Gatica-Perez, S. Bengio, and I. McCowan, “Semi- upervised Adapted HMMs for Unusual Event Detection”, IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 611-618, June 2005.

T. Xiang and S. Gong, “Video Behaviour Profiling and Abnormality Detection without Manual Labelling”, IEEE International Conference on Computer Vision, vol. 2, pp. 1238- 1245, October 2005.

L. Zelnik-Manor and M. Irani, “Event-Based Analysis of Video”, IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 123-130, 2001.

H. Zhong, J. Shi, and M. Visontai, “Detecting Unusual Activity in Video”, IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 819-826, July 2004.

R. Cutler and L. Davis., “Robust real-time periodic motion detection, analysis, and applications”. IEEE Transactions on Pattern Asnalysis and Machine Intelligence, 2000, 22(8):781796.

S. Atev, O. Masoud, N.P. Papanikolopoulos, “Practical mixtures of Gaussians with brightness monitoring,”Proc. IEEE Int. Conf. Intel.Transport. Syst. pp.423–428, October, 2004.

H. Nait-Charif and S. J. McKenna, ”Activity Summarisation and Fall Detection in a Supportive Home Environment,” in International Conference on Pattern Recognition (ICPR), Vol. 4, pp. 323-326, August 2004.

C. Rougier, J. Meunier, A. St-Arnaud and J. Rousseau, ”Fall Detection from Human Shape and Motion History Using Video Surveillance,” nternational Conference on Advanced Information Networking and Applications Workshops (AINAW ), Vol. 2, pp. 875-880, 2007.

B. Yogameena, S. Veeralakshmi, E. Komagal, S. Raju, and V. Abhaikumar “RVM-Based Human Action Classification in Crowd through Projection and Star Skeletonization,” Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2009, Article ID 164019, 12 pagesdoi:10.1155/2009/164019.

C. Stauffer and W.E.L Grimson., “Adaptive background mixture models for real time tracking,” In proceedings of the IEEE Int’l conf.Computer Vision and Pattern recognition pp. 246 -252 1999.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.