Abstract
We present a new scheme to robustly detect a type of human attentive behavior, which we call frequent change in focus of attention (FCFA), from video sequences. FCFA behavior can be easily perceived by people as temporal changes of human head pose (normally the pan angle). For recognition of this behavior by computer, we propose an algorithm to estimate the head pan angle in each frame of the sequence within a normal range of the head tilt angles. Developed from the ISOMAP, we learn a non-linear head pose embedding space in 2-D, which is suitable as a feature space for person-independent head pose estimation. These features are used in a mapping system to map the high dimensional head images into the 2-D feature space from which the head pan angle is calculated very simply. The non-linear person-independent mapping system is composed of two parts: 1) Radial Basis Function (RBF) interpolation, and 2) an adaptive local fitting technique. The results show that head orientation can be estimated robustly. Following the head pan angle estimation, an entropy-based classifier is used to characterize the attentive behaviors. The experimental results show that entropy of the head pan angle is a good measure, which is quite distinct for FCFA and focused attention behavior. Thus by setting an experimental threshold on the entropy value we can successfully and robustly detect FCFA behavior.
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References
Blumberg, B., et al.: Creature smarts: the art and architecture of a virtual brain. In: proc. Game Developers Conf., pp. 147–166 (2000)
Webpage, http://www.isinspect.org.uk/reports/2004/0374_04_r.htm
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, vol. 1, pp. 297–300 (2002)
Matsumoto, Y., Zelinsky, A.: An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement. FG 2000, 499–505 (2000)
Srinivasan, S., Boyer, K.L.: Head pose estimation using view based eigenspaces. ICPR 2002 4, 302–305 (2002)
Everingham, M., Zisserman, A.: Identifying individuals in video by combining generative and discriminative head models. In: Proceedings of the International Conference on Computer Vision, pp. 1103–1110 (2005)
Stiefelhagen, R., Yang, J., Waibel, A.: Modeling focus of attention for meeting indexing. ACM Multimedia, 3–10 (1999)
Tenenbaum, J., de Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Pless, R.: Image spaces and video trajectories: Using isomap to explore video sequences. ICCV 2003 2, 1433–1440 (2003)
Elgammal, A., Lee, C.: Separating style and content on a nonlinear manifold. CVPR 2004, 478–485 (2004)
Vlachos, M., et al.: Non-linear dimensionality reduction techniques for classification and visualization. In: proc. 8th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, pp. 645–651 (2002)
Efros, A., et al.: Seeing through water. In: Neural Information Processing Systems (NIPS 17), pp. 393–400 (2004)
Yang, M., Kriegman, D., Ahuja, N.: Detecting faces in images: A survey. IEEE Trans. PAMI 24, 34–58 (2002)
Fitzgibbon Pilu, M., Fisher, R.: Direct least-square fitting of ellipses. IEEE Trans. PAMI 21, 476–480 (1999)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., Chichester (2000)
Hutcheson, M.: Trimmed Resistant Weighted Scatterplot Smooth. Master’s Thesis, Cornell University (1995)
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© 2006 Springer-Verlag Berlin Heidelberg
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Hu, N., Huang, W., Ranganath, S. (2006). Robust Attentive Behavior Detection by Non-linear Head Pose Embedding and Estimation. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3953. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744078_28
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DOI: https://doi.org/10.1007/11744078_28
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