A multi-class object classifier using boosted Gaussian mixture model

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. © 2010 Springer-Verlag.