Classifying periodic motions in video sequences

In this paper, we present a new technique for separating different types of periodic motions in a video sequence. We consider different motions those that have different periodic patterns with one or many fundamental frequencies. We select the temporal Fourier transform for each pixel to be the representation space for a sequence of images. The classification is performed using nonnegative matrix factorization (NNMF) over the power spectra data set. The paper we present can be applied on a wide range of applications for video sequences analysis, such as: background subtraction on nonstatic backgrounds framework, object segmentation and classification. We point out the fact that no registration technique is applied in the method that we introduce. Nevertheless, this method can be used as a cooperative tool for the existing techniques based on camera motion models (motion segmentation, layer classification, tracking of moving objects, etc).

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