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[en] Object tracking is in our life where it is used in many practical applications such as traffic monitoring, video surveillance, motion-based recognition systems, etc. Object tracking is divided to two main processes namely motion detection and motion estimation (ME). Motion detection occurs simply by subtracting successive frames on pixel by pixel basis. The difference image carries information about motion existence. ME is the process of determining motion vectors of a moving object. Block matching algorithms (BMAs) are the most commonly used motion estimation technique. Its basic idea is dividing the current frame into a matrix of macro blocks that are compared with corresponding blocks in the previous (reference) frame including its adjacent neighbors inside a search window. In this thesis a tracking method using block matching algorithms is presented. The BMA is applied only to the defined region of interest (ROI).Difference technique is first applied on two successive image frames to define ROI where motion occurs. Next, BMA is applied to the defined ROI. The produced motion vectors are scanned and their repetitions are marked. The maximum repetition motion vector is used to represent the motion of the tracked object. This is achieved by grouping the macro blocks that have these repeated motion vectors together. BMAs are grouped to two main categories full search BMA (FSBMA) and fast block matching algorithms (FBMAs).