Earticle

초록

영어

In traffic monitoring system, moving object monitor is the key part of monitoring system. This paper proposed a non-reference background detection method based on the reference background model for the poor detection effect of Gaussian background modeling. The model utilizes a series of sampling values to estimate the probability model of observed pixels before current pixels; Then binaryzation moving object detection based on the probability model. In terms of moving object identification, this paper proposed several features, and being trained and identified through BP neural network. The experimental result indicated that this background model can detect the foreground moving object effectively, and achieved satisfied effect on pedestrians and vehicles target recognition rate.

목차

Abstract
 1. Introduction
 2. Moving Object Detection
 3. The Establishment of Non-Reference Model
  3.1. Density Estimation
  3.2. Variance Estimation
  3.3. Restrain Error Detection
 4. BP Neural Network Classifier Design
  4.1. Feature Extraction
  4.2. Network Structure
  4.3. Learning Algorithm
 5. Experimental Result
 6. Conclusion
 Acknowledgements
 Reference

키워드

저자

• TAO Jian-Ping [ Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China ]
• LV Xiao-lan [ Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China ]
• LIU Jun [ Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605