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Surveillance of public places has become a world-wide concern in recent years. The ability to classify human behaviors in real-time is fundamental to the success of intelligent surveillance systems. The recognition of different human walking trajectory patterns is an important step towards the achievement of this goal. In this research, we utilize the approach of Longest Common Subsequence (LCSS) in determining the similarity between different types of walking trajectories. In order to establish the position and speed boundaries required for the similarity measure, we compare the performance of a number of approaches, including fixed boundary values, variable boundary values, learning boundary by support vector regression, and learning boundary by cascade neural networks. The LCSS similarity approach is also compared with a similarity measure based on hidden Markov model. We found that the boundary establishing method based on learning by support vector regression gives the best results using real-life data during testing.