The Importance Of Ibject Segmentation

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Moving object segmentation is the important task in video surveillance and conferencing systems. Background subtraction methods for a relatively static background are widely exploited for moving object segmentation and detection in image sequences. However there are several issues like noises, shadows, dynamic background and illumination problem. This paper presents a new approach that combines wavelet approach with the Independent Component Analysis (ICA) for segmenting the moving objects. Our study shows improvements on the above mentioned problems and highly tolerable to changes in indoor and outdoor lighting conditions. The experimental results show the superiority of proposed algorithm over popular methods.

Introduction: Reliable detection of moving objects from a video is the main objective for many computer vision applications like, motion detection, video surveillance etc. Moving object segmentation refers to the techniques that extract and locate the objects of interest in an image. There are some techniques that can be used to extract moving objects such as: optical flow, temporal differencing, and background subtraction. In between them background subtraction method is the common approach for identifying the moving objects in image sequences because of their flexibility and effectiveness. Background subtraction depends on subtracting the current image with the background image. Although when we are applying background subtraction method, we need to deal with some critical situations such as: illumination problem, noisy image, sudden change of light, small movements
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Aapo Hyvärinen[3] invented FASTICA, an efficient and fast algorithm for independent component analysis .
In the basic ICA model, observe N linear mixtures x1,x2,…,xn of n independent

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