single-jc.php

JACIII Vol.17 No.5 pp. 746-752
doi: 10.20965/jaciii.2013.p0746
(2013)

Paper:

An Intelligent Security Camera System for Kidnapping Detection

Akira Miyahara and Itaru Nagayama

University of the Ryukyus, 1 Senbaru Nishihara, Okinawa 903-0213, Japan

Received:
March 22, 2013
Accepted:
June 28, 2013
Published:
September 20, 2013
Keywords:
security camera, video processing, crime scene detection, video surveillance
Abstract

In this paper, we propose an automated video surveillance system for kidnapping detection using featurebased characteristics. The localization of moving objects in a video stream and human behavior estimation are key techniques adopted by the proposed system. Some motion characteristics are determined from video streams, and using metrics such as a feature vector, the system automatically classifies the video streams into criminal and non-criminal scenes. The proposed system is called an intelligent security camera. We consider many types of scenarios for the training data set. After constructing the classifier, we use test sequences that are continuous video streams of human behavior consisting of several actions in succession. The experimental results show that the system can effectively detect criminal scenes, such as a kidnapping, by distinguishing human behavior.

Cite this article as:
Akira Miyahara and Itaru Nagayama, “An Intelligent Security Camera System for Kidnapping Detection,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.5, pp. 746-752, 2013.
Data files:
References
  1. [1] National Police Agency of Japan, “Criminal Trends in Japan H23,” 2013,
    http://www.npa.go.jp/toukei/seianki/h23hanzaizyousei.pdf
  2. [2] A. Miyahara, N. Nakazato, and I. Nagayama, “Intelligent Security Camera by Using Motion Analysis and Recognition,” FIT2010 (Fukuoka), DVD, pp. 509-510, 2010.
  3. [3] A. Miyahara and I. Nagayama, “Intelligent Security Camera by Using Modified Skelton Analysis and Recognition,” FIT2011 (Hakodate), DVD, H-061, 2011.
  4. [4] C. Nakajima, S. Sato, Y. Shirai, and H. Ueno, “High-Speed Detection of Intruders from Image Sequences taken by Rotating Camera,” Trans. of IEE, C, Vol.127, No.3, pp. 259-366, 2007.
  5. [5] H. Fujiyoshi, A. J. Lipton, and T. Kanade, “Real-Time Human Motion Analysis by Image Skeltonization,” IEICE TransInf. & Syst., Vol.E87D, No.1, pp. 113-120, 2004.
  6. [6] K. Goya, X. Zhang, and I. Nagayama, “A Method for Automatic Detection of Crimes for Public Security by Using Motion Analysis,” Proc. of the Fifth Int. Conf. Intelligent Information Hiding and Multimedia Signal Processing, CD, Vol.1, pp. 736-741, 2009.
  7. [7] R. Cucchiara, C. Grana, A. Prati, and R. Mezzani, “Probabilistic posture classification for human-behavior analysis,” IEEE Trans. On System, Man and Cybernetics, Vol.35, No.1, Jan. 2005.
  8. [8] A. F. Bobick and J.W. Davis, “The recognition of human movement usingtemporal templates,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.23, No.3, pp. 257-267, Mar. 2001.
  9. [9] K. Sumi, M. Seki, and H. Shiozaki, “Video Analysis Technology for Elevator Cage Abnormality Detection,” IPSJ, Vol.48, No.1, pp. 17-22, 2007.
  10. [10] P. Viola, M. Jones, and D. Snow, “Detecting Pedestrians Using Patterns of Motion and Appearance,” IEEE Int. Conf. on Computer Vision, Vol.2, pp. 734-741, 2003.
  11. [11] O. Ludwig, D. Delgado, V. Goncalves, and U. Nunes, “Trainable Classifier-Fusion Schemes: An Application To Pedestrian Detection,”12th Int. IEEE Conf. On Intelligent Transportation Systems, St. Louis, 2009, V. 1., pp. 432-437, 2009.
  12. [12] T. Nanri and N. Otsu, “Anomaly Detection in Motion Images Containing Multiple Persons,” Trans. IPSJ, Vol.46 (CVIM-12), pp. 43-50, 2005.
  13. [13] T. Matsuyama, T. Wada, H. Habe, and K. Tanahashi, “Background Subtraction under Varying Illumination,” IEICE Trans. on Info. and Sys., Vol.J84-D-II, No.10, pp. 2201-2211, 2001.
  14. [14] M. Seki, T. Wada, H. Fujiwara, and K. Sumi, “Background Subtraction Based on Cooccurrence of Image Variations,” IPSJ Trans., Vol.44, CVIM-6, pp. 54-63, 2003.

*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