Image Segmentation in Video Sequences Using Modified Background Subtraction

Abstract

In computer vision, Background is a technique for finding moving objects in a video sequences for example vehicle driving on a freeway. For to detect non stationary (dynamic) objects, it is necessary to subtracting current image from a time-averaged background image. There are various background subtraction algorithms for detecting moving vehicles or any moving object(s) like pedestrians in urban traffic video sequences. A crude approximation to the task of classifying each pixel on the frame of current image, locate slow-moving objects or in poor image qualities of videos and distinguish shadows from moving objects by using modified background subtraction method. While classifying each pixel on the frame of the current image, it is to be detect the moving object at foreground and background conditional environment that we can classify each pixel using a model of how that pixel looks when it is part of video frame classes. A mixture of Gaussians classification model for each pixel using an unsupervised technique is an efficient, incremental version of Expectation Maximization (EM) is used for the purpose. Unlike standard image-averaging approach, this method automatically updates the mixture component for each video frame class according to likelihood of membership; hence slowmoving objects and poor image quality of videos are also being handled perfectly. Our approach identifies and eliminates shadows much more effectively than other techniques like thresholding.