In recent years, video surveillance systems stand out as an important research topic in the field of computer vision. Studies in this context usually focus on detecting common motion patterns in video sequences, determining unexpected motions or predicting possible future events. Performance of these studies directly depends on the performances of the pre-processing steps. In this paper, we present an approach to extract motion trajectories and then to cluster those trajectories that have similar characteristics. The proposed approach can be considered as a preliminary step for complex surveillance systems. Unlike the existing studies in the literature, the proposed method both produces more accurate results for determining common motion patterns and yields successful results on long video sequences in consequence of used clustering algorithm. The effectiveness and the performance of the proposed approach is validated on VIRAT dataset.