This paper aims to develop an effective method to estimate the human in a frame of video stream. The paper has been threefold, first the preprocessing steps are performed using background subtraction results in foreground extraction and need training images to determine the relationship between foreground pixels and human oriented feature, second an Expectation Maximization based method has been used to cluster individuals in a low resolution scene. The cluster model is used to represent each person. Third the number of people is used as a priori for locating individuals based on feature points. The KLT tracker is used to track the people. Then the methods for estimating the number of people and for locating individuals are connected. Finally, a model is constructed to test the proposed system. Evaluation results on a number of images and videos and comparisons with previous methods are given.

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Expectation And Maximization Cluster Kanade Lucas Tracker