Malichenko Viktor
ABSTRACT
In this paper, we offer a machine learning classifier model, later considered as MLCM, for classifying objects such as road signs and vehicles. Showing the influence of vocabulary size on accuracy of SVM using SURF. Based on SURF method used bag-of-words model as feature extractor. Due to its simplifying representation, it accelerates the first stage of our MLCM. We tested and analyzed accuracy of Support Vector Machines, including Linear, Quadratic and Medium Gaussian SVM as flowed step model and automatically use best result for further estimation. Furthermore, we provide a brief introduction of applied methods and experimental results analysis. MLCM introduces combination of SURF method and several SVMs as well as optimized SVM. This technique shows good performance with minimum failures. Thereafter, it will be implemented for real-time video sequences. The achieved goal can be implemented in the use of self-driving of industrial machines with a safe speed.
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