Abstract
We present an algorithm to detect the attentive behavior of persons with frequent change in focus of attention (FCFA) from a static video camera. This behavior can be easily perceived by people as temporal changes of human head pose. Here, we propose to use features extracted by analyzing a similarity matrix of head pose by using a self-similarity measure of the head image sequence. Further, we present a fast algorithm which uses an image vector sequence represented in the principal components subspace instead of the original image sequence to measure the self-similarity. An important feature of the behavior of FCFA is its cyclic pattern where the head pose repeats its position from time to time. A frequency analysis scheme is proposed to find the dynamic characteristics of persons with frequent change of attention or focused attention. A nonparametric classifier is used to classify these two kinds of behaviors (FCFA and focused attention). The fast algorithm discussed in this paper yields real-time performance as well as good accuracy.
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References
Bauer, B., Jolicoeur, P., Cowan, W.B.: Visual search for color targets that are or are not linearly separatable from distractors. Vision Research 36, 1439–1465 (1996)
Swain, M.J., Ballard, D.H.: Color indexing. Int’l Journal of Computer Vision 7, 11–32 (1991)
Tsotsos, J.K., Culhane, S.M., Wai, W.Y.K., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artificial Intelligence 78, 507–545 (1995)
Cutler, R., Davis, L.: Robust real-time periodic motion detection, analysis, and applications. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 781–796 (2000)
Stiefelhagen, R.: Tracking focus of attention in meetings. In: Proc. Fourth IEEE Int’l Conf. Multimodal Interfaces, pp. 273–280 (2002)
Wu, Y., Toyama, K.: Wide-range person- and illumination-insensitive head orientation estimation. In: Proc. Fourth Int’l Conf. Automatic Face and Gesture Recognition, pp. 183–188 (2000)
Rae, R., Ritter, H.: Recognition of human head orientation based on artificial neural networks. IEEE Trans. Neural Networks 9, 257–265 (1998)
Zhao, L., Pingali, G., Carlbom, I.: Real-time head orientation estimation using neural networks. In: Proc. Int’l Conf. Image Processing (2002)
Krüger, V., Bruns, S., Sommer, G.: Efficient head pose estimation with gabor wavelets. In: Proc. 11th British Machine Vision Conference, vol. 1, pp. 72–81 (2000)
Seitz, S.M., Dyer, C.R.: View-invariant analysis of cyclic motion. Int’l J. Computer Vision 25, 1–23 (1997)
Liu, F., Picard, R.: Finding periodicity in space and time. In: Proc. Int’l Conf. Computer Vision, pp. 376–383 (1998)
Polana, R., Nelson, R.: Detection and recognition of periodic, non-rigid motion. Int’l J. Computer Vision 23, 261–282 (1997)
Zeng, Z., Ma, S.: Head tracking by active particle filtering. In: Proc. Fifth IEEE Int’l Conf. Automatic Face and Gesture Recognition, pp. 82–87 (2002)
Basu, S., Essa, I., Pentland, A.: Motion regularization for model-based head tracking. In: Proc. IEEE Int’l Conf. Pattern Recognition, vol. 3, pp. 611–616 (1996)
Gonzalez, R., Woods, R.: Digital Image Processing. Addison-Wesley Publishing Company, Reading (1992)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., Chichester (2000)
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© 2005 Springer-Verlag Berlin Heidelberg
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Hu, N., Huang, W., Ranganath, S. (2005). Fast Detection of Frequent Change in Focus of Human Attention. In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G. (eds) Attention and Performance in Computational Vision. WAPCV 2004. Lecture Notes in Computer Science, vol 3368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30572-9_17
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DOI: https://doi.org/10.1007/978-3-540-30572-9_17
Publisher Name: Springer, Berlin, Heidelberg
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