Abstract
In this paper we discuss the issues that need to be resolved before fully automated outdoor surveillance systems can be developed, and present solutions to some of these problems. Any outdoor surveillance system must be able to track objects moving in its field of view, classify these objects and detect some of their activities. We have developed a method to track and classify these objects in realistic scenarios. Object tracking in a single camera is performed using background subtraction, followed by region correspondence. This takes into account multiple cues including velocities, sizes and distances of bounding boxes. Objects can be classified based on the type of their motion. This property may be used to label objects as a single person, vehicle or group of persons. Our proposed method to classify objects is based upon detecting recurrent motion for each tracked object. We develop a specific feature vector called a ‘Recurrent Motion Image’ (RMI) to calculate repeated motion of objects. Different types of objects yield very different RMI’s and therefore can easily be classified into different categories on the basis of their RMI. The proposed approach is very efficient both in terms of computational and space criteria. RMI’s are further used to detect carried objects. We present results on a large number of real world sequences including the PETS 2001 sequences. Our surveillance system works in real time at approximately 15Hz for 320x240 resolution color images on a 1.7 GHz pentium-4 PC.
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References
Stauffer C. and Grimson, “Learning Patterns of Activity using Real Time Tracking” IEEE PAMI, Vol. 22, No. 8, August 2000, pp 747–767
C. Wren, A Azarbayejani, T. Darrel and A. Pentland, “PFinder, Real Time Tracking of the Human Body”, IEEE PAMI, vol 19, no. 7 july 1997.
I. Haritaoglu, D. Harwood and L. Davis, “W4: Real time Surveillance of People and Their Activities” IEEE PAMI Vol. 22, No. 8, August 2000.
Y. Ricquebourg and P. Bouthemy, “Real Time Tracking of Moving Persons by Exploiting Spatiotemporal Image Slices ” IEEE PAMI Vol. 22, No. 8, August 2000.
A.Bobick and J. Davis, “The Recognition of Human Movements Using temporal Templates” IEEE PAMI, Vol 23, No. 3, March 2001.
I. K Sethi, R. Jain, “Finding trajectories of feature points in monocular image sequences” IEEE PAMI, Jan 1987.
K. Rangarajan, M. Shah, “Establishing motion correspondence” CVGIP, July 1991.
C. J. Veenman, M.J.T. Reinders, E. Baker, “”Resolving motion correspondence for densely moving points ”IEEE PAMI Jan 2000.
T. Horprasert, D. Harwood and L. Davis, “A Statistical Approach for Real Time Robust Background Subtraction and Shadow Detection “ IEEE Frame Rate Workshop 1999.
P. Rosin and T. Ellis “Image Different Threshold Strategies and Shadow Detection’ 6th British Machine Vision Conf., Birmingham, pp. 347–356 1995
H. Bischof, H. Wildenauer and Ales Leonardis, “Illumination Insensitive Eigenspaces” ICCV, July 2001.
D Jacobs, P Bellhumeur, and R. Basri, “Comparing images under variable lighting” pages 610–617 CVPR 1998.
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© 2002 Springer-Verlag Berlin Heidelberg
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Javed, O., Shah, M. (2002). Tracking and Object Classification for Automated Surveillance. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47979-1_23
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DOI: https://doi.org/10.1007/3-540-47979-1_23
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