自然科学版 英文版
自然科学版 英文版
自然科学版 英文版

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中南大学学报(英文版)

Journal of Central South University

Vol. 17    No. 1    February 2010

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Robust background subtraction in traffic video sequence
GAO Tao(高韬)1, LIU Zheng-guang(刘正光)1, YUE Shi-hong(岳士弘)1, ZHANG Jun(张军)1,
MEI Jian-qiang(梅建强)1, GAO Wen-chun(高文春)2

1. School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China;
2. Honeywell (China) Limited, Tianjin 300042, China

Abstract:For intelligent transportation surveillance, a novel background model based on Marr wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.

 

Key words: background modeling; background subtraction; Marr wavelet; binary discrete wavelet transform; shadow elimination

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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