An algorithm for simultaneous detection, segmentation, and characterization of spatiotemporal periodicity is presented. The use of periodicity templates is proposed to localize and characterize temporal activities. The templates not only indicate the presence and location of a periodic event, but also give an accurate quantitative periodicity measure. Hence, they can be used as a new means of periodicity representation. The proposed algorithm can also be considered as a ``periodicity filter,'' a low-level model of periodicity perception. The algorithm is computationally simple, and shown to be more robust than optical flow based techniques in the presence of noise. A variety of real-world examples are used to demonstrate the performance of the algorithm.
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