Abstract
This paper presents a real-time background estimation and maintenance based people tracking technique in an indoor and an outdoor environments for visual surveillance system. In order to detect foreground objects, first, background scene model is statistically learned using the redundancy of the pixel intensity values during learning stage, even the background is not completely stationary. A background maintenance model is also proposed for preventing some kind of falsies, such as, illumination changes, or physical changes. And then for people detection, candidate foreground regions are detected using thresholding, noise cleaning and their boundaries extracted using morphological filters. From these, a body posture is estimated depending on skeleton of the regions. Finally, the trajectory of the people in motion is implemented for analyzing the people actions tracked in the video sequences. Experimental results demonstrate robustness and real-time performance of the algorithm.
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References
Collins, R.T., Lipton, A.J., Fujiyoshi, H., Kanade, T.: Algorithms for Cooperative Multi sensor Surveillance. Proc. of IEEE 89(10) (2001)
Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-Time Surveillance of People and Their Activities. IEEE Trans. on PAMI 22(8) (2000)
Rosin, P.L., Ellis, T.: Image Difference Threshold Strategies and Shadow Detection. In: Proceeding. British Machine Vision Conference (1995)
Ekinci, M., Nabiyev, V.V.: Vehicle Classifications and Tracking in Traffic Video sequences. In: SIU 2001 9th Signal Proc. and its App., Istanbul (2001) (in Turkish)
Gutshess, D., et al.: A Background Model Initialization Algorithm for Video Surveillance. In: IEEE Int. Conf. on Computer Vision (2001)
Toyama, K., Krumn, J., Brumit, B., Meyers, B.: Wallflower: Principles and Practice of Background Maintenance. In: 7th IEEE Inter. Conf. on Computer Vision (November 1999)
Grimson, W., et al.: Using Adaptive Tracking to Classify and Monitor Activities in a Site. In: Proc. of IEEE Conf. on Computer Vision and Recognition (1998)
Vass, J., Palaniappan, K., Ahuang, X.: Automatic Spatio-Temporal Video Sequence Segmentation. In: Proceeding IEEE Inter. Conf. on Image Processing (1998)
Wren, C., et al.: Pfinder: Real-Time Tracking of the Human Body. IEEE Trans. on Pattern Analysis and Machine Vision Intelligence 19(7) (July 1997)
Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Proc. 6th Eur. Conf. on Computer Vision, Dublin, Ireland (2000)
Long, W., Yang, Y.H.: Stationary Background Generation: An alternative to the Difference of Two Images. Pattern Recognition 23(12) (1990)
Yang, Y.H., Levine, M.D.: The Background Primal Sketch: An Approach for tracking moving objects. Machine Vision and Applications 5 (1992)
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© 2003 Springer-Verlag Berlin Heidelberg
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Ekinci, M., Gedikli, E. (2003). Background Estimation Based People Detection and Tracking for Video Surveillance. In: Yazıcı, A., Şener, C. (eds) Computer and Information Sciences - ISCIS 2003. ISCIS 2003. Lecture Notes in Computer Science, vol 2869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39737-3_53
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DOI: https://doi.org/10.1007/978-3-540-39737-3_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20409-1
Online ISBN: 978-3-540-39737-3
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