Abstract
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 Non-Negative 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 non-static 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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cohen, C., Conway, L., Koditschek, D.: Dynamical system representation, generation, and recognition of basic oscillatory motion gestures. In: Int. Conf. Auto. Face and Gesture Recognition, vol. 1, pp. 60–65 (1996)
Cutler, R., Davis, L.: Real-time periodic motion detection, analysis, and applications. In: Computer Vision and Pattern Recognition, vol. 1, pp. 326–332 (1999)
Goddard, N.: The interpretation of visual motion: Recognizing moving lights. In: IEEE Worshop on Motion, vol. 1, pp. 212–220 (1989)
Johansson, G.: Visual motion perception. Scientific American 232, 75–88 (1976)
Lee, D.D., Seung, H.S.: Learning the parts of objects with nonnegative matrix factorization. Nature 401, 788–791 (1999)
Little, J., Boyd, J.: Recognizing people by their gate: the shape of motion. In: Videre, vol. 1 (1998)
Liu, F., Picard, R.: Finding periodicity in space and time. In: International Conference on Computer Vision, vol. 1, pp. 376–383 (1998)
Niyogi, S., Adelson, E.: Analyzing and recognizing walking figures in xyt. In: Computer Vision and Pattern Recognition, vol. 1, pp. 469–474 (1994)
Polana, R., Nelson, R.: Low level recognition of human motion. In: IEEE Worshop on Motion of Non-rigid and Articulated Objects, vol. 1, pp. 77–82 (1994)
Polana, R., Nelson, R.: Detection and recognition of periodic, non-rigid motion. International Journal of Computer Vision 23, 261–282 (1997)
Seitz, S., Dyer, C.: View-invariant analysis of cyclic motion. International Journal of Computer Vision 25, 1–23 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Orriols, X., Binefa, X. (2003). Analyzing Periodic Motion Classification. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_78
Download citation
DOI: https://doi.org/10.1007/978-3-540-44871-6_78
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40217-6
Online ISBN: 978-3-540-44871-6
eBook Packages: Springer Book Archive