Abstract
This paper proposes decision fusion method of shape and motion information based on Bayesian framework for object classification in image sequences. This method is designed for intelligent information and surveillance guard robots to detect and track a suspicious person and vehicle within a security region. For reliable and stable classification of targets, multiple invariant feature vectors to more certainly discriminate between targets are required. To do this, shape and motion information are extracted using Fourier descriptor, gradients, and motion feature variation on spatial and temporal images, and then local decisions are performed respectively. Finally, global decision is done using decision fusion method based on Bayesian framework. The experimental results on the different test sequences showed that the proposed method obtained good classification result than any other ones using neural net and other fusion methods.
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References
Collins, R.T., Lipton, A.J., Kanade, T.: Introduction to the special section on video surveillance. IEEE Trans. Pattern Anal. Machine Intell. 22, 745 (2000)
Lipton, A.J., Fujiyosi, H., Patil, R.S.: Moving target classification and tracking from real-time video. In: Proc of IEEE Workshop. on Applications of Computer Vision, pp. 8–14 (1998)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc of IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. Proc of Int. J. Comput. Vis. 12(1), 42–77
Haritaoglu, I., Harwood, D., Davis, L.S.: A real-time system for detecting and tracking people. In: Proc. of Int. Conf. on Face and Gesture Recognition, April 1998, pp. 222–227 (1998)
Kuno, Y., Watanabe, T., Shimosakoda, Y., Nakagawa, S.: Automated detection of human for visual surveillance system. In: Proc. of Int. Conf. on Pattern Recognition, pp. 865–869 (1996)
Cutler, R., Davis, L.S.: Robust real-time periodic motion detection, analysis, and applications. IEEE Trans. Pattern Anal. Machine Intell. 22, 781–796 (2000)
Fujiyosi, H., Tomasik, J.A.: Real-time human motion analysis by image skeletonization. IEICE Trans. on Info & Systems E87-D(1) (January 2004)
Kokar, M.M., Tomasik, J.A.: Data vs. decision fusion in the category theory framework. In: Proc. 2nd Int. Conf. on Information Fusion (2001)
Li, X.R., Zhu, Y., Wang, J., Han, C.: Optimal linear estimation fusion-Part I: Unified fusion rules. IEEE Trans. Information Theory 49(9) (September 2003)
Lee, H., Beh, J., Kim, J., Ko, H.: Model based Abnormal Acoustic Source Detection Using a Microphone Array. In: Zhang, S., Jarvis, R. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 966–969. Springer, Heidelberg (2005)
Bar-Shalom, Y., Li, X.R.: Multitarget-multisensor tracking: principles and techniques. YBS Press (1995)
Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House (1999)
Brooks, R.R., Iyengar, S.S.: Multi-Sensor Fusion: Fundamentals and Applications with software. Prentice-Hall, Englewood Cliffs (1998)
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Lee, H., Kim, J., Kim, J. (2006). Decision Fusion of Shape and Motion Information Based on Bayesian Framework for Moving Object Classification in Image Sequences. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_4
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DOI: https://doi.org/10.1007/11875604_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
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