Abstract
Behavior determination and multiple object tracking for video surveillance are two of the most active fields of computer vision. The reason for this activity is largely due to the fact that there are many application areas. This paper describes work in developing software algorithms for the tele-assistance for the elderly, which could be used as early warning monitor for anomalous events.We treat algorithms for both the multiple object tracking problem as well simple behavior detectors based on human body positions. There are several original contributions proposed by this paper. First, a method for comparing foreground - background segmention is proposed. Second a feature vector based tracking algorithm is developed for discriminating multiple objects. Finally, a simple real-time histogram based algorithm is described for discriminating movements and body positions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly, Cambridge (2008)
Forsyth, D.A., Arikan, O., Ikemoto, L., O’Brien, J., Ramanan, D.: Computational studies of human motion: part 1, tracking and motion synthesis. Found. Trends. Comput. Graph. Vis. 1(2-3), 77–254 (2005)
Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 34(3), 334–352 (2004)
Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for realtime tracking with shadow detection. In: Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01, VIDEO BASED SURVEILLANCE SYSTEMS: Computer Vision and Distributed Processing (September 2001)
Meeds, E.W., Ross, D.A., Zemel, R.S., Roweis, S.T.: Learning stick-figure models using nonparametric bayesian priors over trees. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (June 2008)
Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976–990 (2010)
Department of Economic United Nations and Social Affairs Population Division. World population ageing 2009. Technical report (2010), http://www.un.org/esa/population/publications/WPA2009/WPA2009-report.pdf
Wei, Z., Bi, D., Gao, S., Xu, J.: Contour tracking based on online feature selection and dynamic neighbor region fast level set. In: Fifth International Conference on Image and Graphics, ICIG 2009, pp. 238–243 (September 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Conde, I.G., Cecchi, D.O., Sobrino, X.A.V., Rodríguez, Á.O. (2011). Intelligent Video Monitoring for Anomalous Event Detection. In: Novais, P., Preuveneers, D., Corchado, J.M. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent and Soft Computing, vol 92. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19937-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-19937-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19936-3
Online ISBN: 978-3-642-19937-0
eBook Packages: EngineeringEngineering (R0)