Abstract
In this paper, we present a real-time surveillance system that is suitable for the indoor environment. The system is designed to detect, track and recognize the behavior of humans, using a single static camera. Background subtraction is applied to extract moving objects; these objects are tracked using linear approximation. Shadow regions are detected and removed using linear dependence and spatial connectivity properties of the shadow regions. Pattern matching and TDL (Two Dimensional Logarithmic) search approach are used to solve the problem of the occlusion of objects and depth reasoning. Behaviors of moving objects are detected by examining the sequence of shapes extracted from the scene. Shapes of moving objects are interpreted as characters of an alphabet. Each character represents a class of similar blob shapes classified using K-Means clustering. The model is used to recognize behaviors in an office with promising results.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
O. Masoud and N. ikolaos P. Papanikolopoulos, “A Novel Method for Tracking and Counting Pedestrains in Real-Time Using a Single Camera,” IEEE Trans. On Vehicular Tech. Vol. 50, No. 5 Sep 2001.
D. Koller, J. Weber and J. Malik, “Robust Multiple Car Tracking with Occlusion Reasoning,” In Proceedings of Third European Conference on Computer Vision, Stockholm, Sweden, May 2–6, 1994, pp. 189–196, LNCS 800, Springer-Verlag, 1994.
R. Cucchiara, C. Grana, A. Prati and R. Vezzani, “Computer vision system for in-house video surveillance,” In IEE Proceedings of Visual Image Signal Process. Vol. 152, No. 2, April 2005.
I. Haritaoglu, Larry S. Davis and D. Harwood. w4 who? when? where? what? a real time system for detecing and tracking people,” IEEE Trans, on Pattern Analysis and Machine Intelligence, Vol.22 Aug 2000.
C. Wren, A. Azarbayejani, T. Darrell and A. Pentland, “Pfinder: Real-time Tracking of the human body,” IEEE Trans. on Pattern Analysis and Machine Intell., 19(7):780–785, 1997.
S.Y. Chien, S.Y. Ma and L.G. Chen, “Efficient Moving Object Segmentation Algorithm Using Background Registration Technique,” IEEE Trans. on Circuits and Systems for Video Tech. Vol. 12 No. 7 July 2002.
Jaswant R. Jain, Anil K. Jain, “Displacement measurement and its application in interframe image coding,” IEEE Transactions on Communications, Volume COM-29, Number 12, p 1799–1808, December 1981
R. Cucchiara, C. Grana, M. Piccardi, A. Prati and S. Sirotti, “Detecting Moving Objects, Ghosts, and Shadows in Video Streams,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, pp. 1337–1342, 2003.
M-K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. on Information Theory, IT-8:pp. 179–187, 1962.
A. Khotanzad and Y. H. Hongs, “Invariant image recognition by Zernike moments,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 12(5):pp. 489–497,1990
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 International Federation for Information Processing
About this paper
Cite this paper
Lin, M.W., Tapamo, J.R. (2006). A Model of Real-Time Indoor Surveillance System using Behavior Detection. In: Maglogiannis, I., Karpouzis, K., Bramer, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2006. IFIP International Federation for Information Processing, vol 204. Springer, Boston, MA . https://doi.org/10.1007/0-387-34224-9_24
Download citation
DOI: https://doi.org/10.1007/0-387-34224-9_24
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-34223-8
Online ISBN: 978-0-387-34224-5
eBook Packages: Computer ScienceComputer Science (R0)