An Intelligent Automatic Surveillance System via Fuzzy Rule Base System and Genetic Algorithms
Hyung-Jin Kang*, Heejin Lee*, Heung-Sik Noh*, Jung-Hwan Kim*, Dong-Yon Kim**, Mignon Park*
*Intelligent Control and System Lab., Dept. of Electronic Eng., Yonsei University 134 Shinchon-dong, Seodaemun-gu, Seoul, 120-749, Korea
**Dept. of Electronic Eng., Ansung National University 67 Sukjung-dong, Ansung, Kyungki, 456-749, Korea
In this paper, an intelligent Automatic Surveillance System is proposed using fuzzy rule base system and genetic algorithms. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas of the segmented regions are used in this paper. These features are not unique and decisive characteristics for representing human. Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from that information, we use a fuzzy rule base system as an approximate reasoning method The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic information and then, trained and tested with input/output data. For the effective training, the well-known Genetic Algorithms (GA) are used. Experimental results are shown, demonstrating the validity of our system.
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