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For intelligent surveillance, one of the major tasks to achieve is to recognize activities present in the scene of interest. Human subjects are the most important elements in a surveillance system and it is crucial to classify human actions. In this paper, we tackle the problem of classifying human actions as running or walking in videos. We propose using local temporal features extracted from rectangular boxes that surround the subject of interest in each frame. We test the system using a database of hand-labeled walking and running videos. Our experiments yield a low 2.5% classification error rate using period-based features and the local speed computed using a range of frames around the current frame. Shorter range time-derivative features are not very useful since they are highly variable. Our results show that the system is able to correctly recognize running or walking activities despite differences in appearance and clothing of subjects.