Abstract
A novel background model based on Marr wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms are introduced. The background model keeps a sample of intensity values for each pixel in the image and uses this sample to estimate the probability density function of the pixel intensity. The density function is estimated using a new Marr wavelet kernel density estimation technique. Since this approach is quite general, the model can approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame are transformed in the binary discrete wavelet domain, and background subtraction is performed in each sub-band. After obtaining the foreground, shadow is eliminated by an edge detection method. Experiments show that the simple method produces good results with much lower computational complexity and can effectively extract the moving objects, even though the objects are similar to the background, and shadows can be successfully eliminated, thus good moving objects segmentation can be obtained.
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Gao, T., Liu, Zg., Gao, Wc., Zhang, J. (2009). A Robust Technique for Background Subtraction and Shadow Elimination in Traffic Video Sequence. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_32
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DOI: https://doi.org/10.1007/978-3-642-02568-6_32
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