Abstract
The analysis of periodic or repetitive motions is useful in many applications, both in the natural and the man-made world. An important example is the recognition of human and animal activities. Existing methods for the analysis of periodic motions first extract motion trajectories, e.g. via correlation, or feature point matching. We present a new approach, which takes advantage of both the frequency and spatial information of the video. The 2D spatial Fourier transform is applied to each frame, and time-frequency distributions are then used to estimate the time-varying object motions. Thus, multiple periodic trajectories are extracted and their periods are estimated. The period information is finally used to segment the periodically moving objects. Unlike existing methods, our approach estimates multiple periodicities simultaneously, it is robust to deviations from strictly periodic motion, and estimates periodicities superposed on translations. Experiments with synthetic and real sequences display the capabilities and limitations of this approach. Supplementary material is provided, showing the video sequences used in the experiments.
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References
Boyd, J., Little, J.: Motion from transient oscillations. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, CVPR (2001)
Seitz, S., Dyer, C.R.: View-invariant analysis of cyclic motion. International Journal of Computer Vision 25, 231–251 (1997)
Tsai, P., Shah, M., Keiter, K., Kasparis, T.: Cyclic motion detection for motion based recognition. Pattern Recognition 27, 1591–1603 (1994)
Cutler, R., Davis, L.S.: Robust real-time periodic motion detection, analysis, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 781–796 (2000)
Polana, R., Nelson, R.: Detection and recognition of periodic, nonrigid motion. International Journal of Computer Vision 23, 261–282 (1997)
Lu, C., Ferrier, N.: Repetitive motion analysis: Segmentation and event classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 258–263 (2004)
Cohen, L.: Time-frequency distributions-a review. Proceedings of the IEEE 77, 941–981 (1989)
Pepin, M.P., Clark, M.P.: On the performance of several 2-d harmonic retrieval techniques. In: Conference Record of the Twenty-Eighth Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 254–258 (1994)
Briassouli, A., Ahuja, N.: Fusion of frequency and spatial domain information for motion analysis. In: ICPR 2004, Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 175–178 (2004)
Chen, W., Giannakis, G.B., Nandhakumar, N.: A harmonic retrieval framework for discontinuous motion estimation. IEEE Transactions on Image Processing 7, 1242–1257 (1998)
Kojima, A., Sakurai, N., Kishigami, J.I.: Motion detection using 3D-FFT spectrum. In: 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. 213–216 (1993)
Czerwinski, R., Jones, D.: Adaptive short-time Fourier analysis. IEEE Signal Processing Letters 4, 42–45 (1997)
Boashash, B.: Estimating and interpreting the instantaneous frequency of a signal - Part 1: Fundamentals. Proceedings of the IEEE 80, 520–538 (1992)
Djurovic, I., Stankovic, S.: Estimation of time-varying velocities of moving objects by time-frequency representations. IEEE Transactions on Signal Processing 47, 493–504 (1999)
Kay, S.M.: Modern Spectral Estimation, Theory and Applications. Prentice-Hall, Englewood Cliffs (1988)
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Briassouli, A., Ahuja, N. (2006). Estimation of Multiple Periodic Motions from Video. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744023_12
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DOI: https://doi.org/10.1007/11744023_12
Publisher Name: Springer, Berlin, Heidelberg
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