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Object Detection and Tracking using Bayesian Classifier in Surveillance

서베일런스에서 베이지안 분류기를 이용한 객체 검출 및 추적

  • 강성관 (인하대학교 컴퓨터정보공학과) ;
  • 최경호 (경기대학교 산업기술보호특화센터) ;
  • 정경용 (상지대학교 컴퓨터정보공학부) ;
  • 이정현 (인하대학교 컴퓨터정보공학과)
  • Received : 2012.06.10
  • Accepted : 2012.07.10
  • Published : 2012.07.31

Abstract

In this paper, we present a object detection and tracking method based on image context analysis. It is robust from the image variations such as complicated background, dynamic movement of the object. Image context analysis is carried out using the hybrid network of k-means and RBF. The proposed object detection employs context-driven adaptive Bayesian framework to relive the effect due to uneven object images. The proposed method used feature vector generator using 2D Haar wavelet transform and the Bayesian discriminant method in order to enhance the speed of learning. The system took less time to learn, and learning in a wide variety of data showed consistent results. After we developed the proposed method was applied to real-world environment. As a result, in the case of the object to detect pass outside expected area or other changes in the uncertain reaction showed that stable. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

Keywords

Intrusion Detection;Image Tracking;Object Tracking and Detection;Bayesian Classifier

Acknowledgement

Supported by : 지식경제부