Robust background subtraction in traffic video sequence

• Published in: Journal of Central South University of Technology. Science & technology of mining and metallurgy Vol. 17; no. 1; pp. 187 - 195
• Main Authors: Gao, Tao, Liu, Zheng-guang, Yue, Shi-hong, Zhang, Jun, Mei, Jian-qiang, Gao, Wen-chun
• Format: Journal Article
• Language: English
• Published: Heidelberg Central South University 01.02.2010
• Subjects: Engineering; Metallic Materials; shadow elimination; binary discrete wavelet transform; background modeling; background subtraction; Marr wavelet
• ISSN: 1005-9784; 1993-0666
• DOI: 10.1007/s11771-010-0029-z

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.