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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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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