A framework for moving object segmentation under rapidly changing illumination conditions in complex wavelet domain

Moving object segmentation using change detection in wavelet domain under dynamic background changes is a challenging problem in video surveillance system. There are several literature surveys available in change detection using wavelet domain for moving object segmentation but most of the research work are based on static background changes. Change detection under background changes is a challenging task and it has not been addressed in effectively in literature. To address this issues, a fast and robust moving object segmentation approach is proposed in dynamic background changes which consist of six steps applied on given video frames which include: wavelet decomposition of frames using complex wavelet transform; use of change detection on detail coefficients (LH, HL, HH); use of background modeling on approximate co-efficient (LL sub-band); strong edge detection; inverse wavelet transformation for reconstruction; and finally using closing morphology operator. For dynamic background modeling, we have improved the Gaussian mixture model and use mode value to find the variance of K-Gaussian. A comparative analysis of the proposed method is presented both quantitatively and qualitatively with other standard methods available in the literature. The various performance measure used for quantitative analysis include RFAM, RPM, NCC and MP. From the obtained result, it is observed that proposed approach is performing better in comparison to other methods in consideration.