Framework for dynamic background modeling and shadow suppression for moving object segmentation in complex wavelet domain

Abstract. Moving object segmentation using change detection in wavelet domain under continuous variations of lighting condition is a challenging problem in video surveillance systems. There are several methods proposed in the literature for change detection in wavelet domain for moving object segmentation having static backgrounds, but it has not been addressed effectively for dynamic background changes. The methods proposed in the literature suffer from various problems, such as ghostlike appearance, object shadows, and noise. To deal with these issues, a framework for dynamic background modeling and shadow suppression under rapidly changing illumination conditions for moving object segmentation in complex wavelet domain is proposed. The proposed method consists of eight steps applied on given video frames, which include wavelet decomposition of frame using complex wavelet transform; use of change detection on detail coefficients (LH, HL, and HH), use of improved Gaussian mixture-based dynamic background modeling on approximate coefficient (LL subband); cast shadow suppression; use of soft thresholding for noise removal; strong edge detection; inverse wavelet transformation for reconstruction; and finally using closing morphology operator. A comparative analysis of the proposed method is presented both qualitatively and quantitatively with other standard methods available in the literature for six datasets in terms of various performance measures. Experimental results demonstrate the efficacy of the proposed method.