Now a day’s spread of super computers, existing of high resolution and low-priced video cameras, and increasing the computerized video analysis has made more curiosity in tracking algorithms. Automatic identification and tracing of multiple moving objects through video scene is an interesting field of computer visualization. Identification and tracking of multiple people is a vital and challenging task for many applications like human-computer interface, video communication, security application and surveillance system. Various researchers offer various algorithms but none of this was work properly to distinguish the players automatically when creating occlusion. The first step to tracking multiple objects in video sequence is detection. Background subtraction is a very popular and effective method for foreground detection (assuming that background should be stationary). In this thesis we apply various background subtraction methods to tackle the difficulties like changing illumination condition, background clutter and camouflage. The method we propose to overcome this problem is operates the background subtraction by calculating the Mahalanobis distances. The second step to track multiple moving objects in soccer scene by using particle filters method that estimate the non-Gaussian, non-linear state-space model, which is a multi-target tracking method. These methods are applied on real soccer video sequences and the result show that it is successfully track and distinguish the players. After tracking is done by using multi camera views, we collecting the data from all cameras and creating geometrical relationship between cameras called Homography
