Models which describe road traffic patterns can be helpful in detection and/or prevention of uncommon and dangerous situations. Such models can be built by the use of motion detection algorithms applied to video data. Block matching is a standard technique for encoding motion in video compression algorithms. We explored the capabilities of the block matching algorithm when applied for object tracking. The goal of our experiments is two-fold: (1) to explore the abilities of the block matching algorithm on low resolution and low frame rate video and (2) to improve the motion detection performance by the use of different search techniques during the process of block matching. Our experiments showed that the block matching algorithm yields good object tracking results and can be used with high success on low resolution and low frame rate video data. We observed that different searching methods have small effect on the final results. In addition, we proposed a technique based on frame history, which successfully overcame false motion caused by small camera movements.