single-jc.php

JACIII Vol.12 No.1 pp. 4-9
doi: 10.20965/jaciii.2008.p0004
(2008)

Paper:

Detection Algorithm for Real Surveillance Cameras Using Geographic Information

Yutaka Hatakeyama*, Akimichi Mitsuta**, and Kaoru Hirota***

*Center of Medical Information Science, Medical School, Kochi University, Kohasu, Oko-cho, Nankoku city, Kochi 783-8505, Japan

**Customer Solutions Development Co., Ltd., Japan, 3-2-1 Sakado, Takatsu-ku, Kawasaki-city, Kanagawa 213-0012, Japan

***Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Received:
March 27, 2007
Accepted:
September 28, 2007
Published:
January 20, 2008
Keywords:
surveillance system, detection algorithm, color similarity, multiple camera
Abstract

Detection algorithm for pedestrians is proposed for the real surveillance system based on color similarity for dynamic color images under low illumination, where the proposed color similarity is defined by color change vectors in the L*a*b* color metric space and the time taken by pedestrians to pass between surveillance camera. It provides continuous detection results through surveillance cameras under lower luminance conditions in real surveillance system. Experimental results for dynamic image taken under low illumination in streets show that detected frames with the proposed algorithm increased by 20% compared to detection results without geographic information. The proposed algorithm is being considered for use in poor security areas in downtown Japan.

Cite this article as:
Yutaka Hatakeyama, Akimichi Mitsuta, and Kaoru Hirota, “Detection Algorithm for Real Surveillance Cameras Using Geographic Information,” J. Adv. Comput. Intell. Intell. Inform., Vol.12, No.1, pp. 4-9, 2008.
Data files:
References
  1. [1] P. Spagnolo, T. D’Orazio, M. Leo, and A. Distante, “Moving object segmentation by background subtraction and temporal analysis,” Image and Vision Computing, Vol.24, pp. 411-423, 2006.
  2. [2] C. Motamed, “Motion detection and tracking using belief indicators for an automatic visual-surveillance system,” Image and Vision Computing, Vol.24, pp. 1192-1201, 2006.
  3. [3] B. Lei and L. Xu, “Real-time outdoor video surveillance with robust foreground extraction and object tracking via multi-state transition management,” Pattern Recognition Letters, Vol.27, pp. 1816-1825, 2006.
  4. [4] L. M. Fuentes and S. A. Velast, “People tracking in surveillance applications,” Image and Vision Computing, Vol.24, pp. 1165-1171, 2006.
  5. [5] L. Wang, W. Hu, and T. Tan, “Recent developments in human motion analysis,” Pattern Recognition, Vol.36, pp. 585-601, 2003.
  6. [6] E. Stringa and C. S. Regazzoni, “Real-time video-shot detection for scene surveillance applications,” Vol.9, Issue 1, pp. 69-79, 2000.
  7. [7] Y. Hatakeyama, K. Kawamoto, H. Nobuhara, S. Yoshida, and K. Hirota, “Color Instance-Based Reasoning and its Application to Dynamic Image Restoration Under Low Luminance Conditions,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.8, No.6, pp. 639-648, 2004.
  8. [8] Y. Hatakeyama, K. Kawamoto, H. Nobuhara, S. Yoshida, and K. Hirota, “Color Restration Algorithm for Dynamic Images under Multiple Luminance Conditions using Correction Vectors,” Pattern Recognition Letters, Vol.26, pp. 1304-1315, 2005.
  9. [9] Y. Hatakeyama, A. Tamura, A. Mitusuta, and K. Hirota, “Detecting Algorithm for Surveillance System using Dynamic Color Images under Multiple Luminance Conditions,” 2005 Int. Symposium on Advanced Intelligent Systems (ISIS2005), (Yeosu, Korea), pp. 404-407, 2005.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Sep. 14, 2021