Analyzing Transformation of 1D-Functions for Frequency Domain based Video Classification

In this paper we illuminate a frequency domain based classification method for video scenes. Videos from certain topical areas often contain activities with repeating movements. Sports videos, home improvement videos, or videos showing mechanical motion are some example areas. Assessing main and side frequencies of each repeating movement gives rise to the motion type. We obtain the frequency domain by transforming spatio-temporal motion trajectories. Further on we explain how to compute frequency features for video clips and how to use them for classifying. The focus of the experimental phase is on transforms utilized for our system. By comparing various transforms, experiments show the optimal transform for a motion frequency based approach.

Authors:

• Kahraman Ayyildiz
• Stefan Conrad

Keywords:

• action recognition
• frequency
• transform
• motion recognition
• repeating movement
• video classification

References:

[1] N. Ahmed, T. Natarajan, and K. R. Rao, "Discrete Cosine Transform,"
IEEE Trans. Computers, 90-93, 1974.
[2] K. Ayyildiz and S. Conrad, "Video classification by main frequencies
of repeating movements," in 12th International Workshop on Image
Analysis for Multimedia Interactive Services, April 13-15, 2011.
[3] Q.G. Meng, B.H. Li, and H. Holstein, "Recognition of human periodic
movements from unstructured information using a motion-based
frequency domain approach," IVC, 795-809, 2006.
[4] James W. Cooley, and John W. Tukey, "An algorithm for the machine
calculation of complex Fourier series," Math. Comp. 19, 297-301, 1965.
[5] W. Wong, W. Siu, and K. Lam, "Generation of moment invariants and
their uses for character recognition," Patt. Recog. Lett., 115-123, 1995.
[6] S.C. Pei and F. Chen, "Semantic scenes detection and classification in
sports videos," in Conf. on Comp. Vision, Graphics and Image Proc.,
2003, 210-217.
[7] R. Lienhart, "Indexing and retrieval of digital video sequences based on
automatic text recognition," in Fourth ACM int. Conf. on Multimedia,
419-420, 1996.
[8] S. A. Martucci, "Symmetric convolution and the discrete sine and cosine
transforms," IEEE Trans. Sig. Processing SP-42, 1038-1051, 1994.
[9] N. Patel and I. Sethi, "Audio characterization for video indexing," in
SPIE on Storage and Retrieval for Still Image and Video Databases,
373-384, 1996.
[10] Fangxiang Cheng, William Christmas, and Josef Kittler, "Periodic
human motion description for sports video databases," International
Conference on Pattern Recognition, vol. 3, 870-873, 2004.
[11] Matteo Frigo, "A fast Fourier transform compiler," SIGPLAN Notices,
642-655, 2004.
[12] G. Beylkin, R. Coifman, and V. Rokhlin, "Fast wavelet transforms and
numerical algorithms," Comm. Pure Appl. Math., 141-183, 1991.
[13] Hakob Sarukhanyan, Sos Agaian, Karen Egiazarian, and Jaakko Astola,
"Reversible Hadamard Transforms," ELEC. ENERG., 309-330, 2007.
[14] B.J. Fino, and V.R. Algazi, "Unified Matrix Treatment of the Fast Walsh
Hadamard Transform," IEEE Trans. on Comp., 1142-1146, 1976.
[15] O. Hunt and R. Mukundan, "A comparison of discrete orthogonal basis
functions for image compression," Image and Vision Computing New
Zealand, 234-245, 2004.
[16] Ross Cutler and Larry S. Davis, "Robust real-time periodic motion
detection, analysis, and applications," IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 22, no. 8, 781-796, 2000.
[17] Hozumi Tanaka, "Hadamard transform for speech wave analysis," in
Technical Report at Stanford University, 1972.
[18] P. Tsai, M. Shah, K. Keiter, and T. Kasparis, "Cyclic motion detection,"
in Pattern Recognition, 1591-1603, 1994.
[19] H. B. Kekre, S. D. Thepade, and A. Maloo, "Performance comparison of
image retrieval using fractional coefficients of transformed image using
dct, walsh, haar and kekres transform," Int. Journal of Image Proc.
(IJIP), 142-155, 2010.
[20] YouTube LLC. Youtube: Broadcast yourself. www.youtube.com.