Occupant Monitoring System for Traffic Control Based on Visual Categorization

Abstract

This paper presents the basics of Bag of visual words method, which will be used for an occupant monitoring system that integrates a small onboard camera inside vehicles. It is intended to detect passengers' faces because it is the most appealing characteristic of occupants in a vehicle. This work proposes the implementation of visual categorization by means of two classification methods (Naïve Bayes and Multi-class SVM) that build multi-category image models using the invariant descriptors (SIFT and SURF) extracted from the images under analysis. Bag of visual words approach requires training in order to cluster invariant descriptors and learn the data distribution depending on the classification algorithm. Once the model is created, the category of every test image can be determined by querying a visual dictionary like searching a word in a text dictionary. The performance of the classifiers will be evaluated doing several comparative tests and using standard multi-category image databases. Experimental results and the conclusions are presented.