Abstract
We propose a new object classification model, which is applied to a computer-vision-based traffic surveillance system. The main issue in this paper is to recognize various objects on a road such as vehicles, pedestrians and unknown backgrounds. In order to achieve robust classification performance against translation and scale variation of the objects, we propose new C1-like features which modify the conventional C1 features in the Hierarchical MAX model to get the computational efficiency. Also, we develop a new adaptively boosted Gaussian mixture model to build a classifier for multi-class objects recognition in real road environments. Experimental results show the excellence of the proposed model for multi-class object recognition and can be successfully used for constructing a traffic surveillance system.
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References
Wijnhoven, R., De With, P.H.N.: 3D Wire-frame Object-modeling Experiments for Video Surveillance. In: Proc. of 27th Symp. Inform. Theory in the Benelux, pp. 101–108 (2006)
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust Object Recognition with Cortex-like Mechanisms. Trans. on Pattern Analysis and Machine Intelligence (PAMI) 29(3), 411–426 (2007)
Wijnhoven, R., De With, P.H.N.: Patch-based Experiments with Object Classification in Video Surveillance. In: Blanc-Talon, J., et al. (eds.) ACIVS 2007. LNCS, vol. 4678, pp. 285–296. Springer, Heidelberg (2007)
Woo, J.-W., Lim, Y.-C., Lee, M.: Obstacle Categorization Based on Hybridizing Global and Local Features. In: Chan, J.H. (ed.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 1–10. Springer, Heidelberg (2009)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. J. Data Min. Knowl. Discov. 2, 121–167 (1998)
Scherrer, B.: Gaussian Mixture Model Classifiers (2007)
Schapire, R.E.: The Strength of Weak Learnability. Machine Learning 5, 197–227 (1990)
Freund, Y., Schapire, R.E.: A Short Introduction to Boosting. The Japanese Society for Artificial Intelligence 14(5), 771–780 (1999)
Riesenhuber, M., Poggio, T.: Hierarchical Models of Object Recognition in Cortex. J. Neurosci. 2, 1019–1025 (1999)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. J. of the Royal Statistical Society 39(1), 1–38 (1977)
 Blimes, J. A.: A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Technical Report ICSI-TR-97-021 (1997)
Verbeek, J., Vlassis, N., Krose, B.: Efficient Greedy Learning of Gaussian Mixture Models. Neural Computation 15, 469–485 (2003)
Center for Biological & Computational Learning, MIT, http://cbcl.mit.edu
Computational Vision Lab, Caltech, http://www.vision.caltech.edu
Daimler Pedestrian Detection Benchmark Data Set, http://www.gavrila.net
Artificial Brain Research Lab, Kyungpook National University, http://abr.knu.ac.kr/ABR_surveillance_DB.html
Fergus, R., Perona, P., Zisserman, A.: Object Class Recognition by Unsupervised Scale-Invariant Learning. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 264–271 (2003)
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Lee, W., Lee, M. (2010). A Multi-class Object Classifier Using Boosted Gaussian Mixture Model. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_53
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DOI: https://doi.org/10.1007/978-3-642-17537-4_53
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