The Strength of Weak Learnability
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Efficient greedy learning of Gaussian mixture models
Neural Computation
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Obstacle Categorization Based on Hybridizing Global and Local Features
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Patch-based experiments with object classification in video surveillance
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
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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.