Objects recognition in visible and infrared images from the road scene

The detection of an obstacle in a traffic scene situation (obstacle which most often means a pedestrian or a vehicle) is a real challenge due to the outdoor environment and the variety of appearance of the obstacle. In this paper some details about our recognition module applied on visible and infrared image databases are presented. Given an image, or a region within an image, generate different types of features (Haar and Gabor wavelet, seven statistics moments, eight most important DCT coefficients and some GLCM coefficients) that will be fed to a classifier, in order to classify the image in one of the 5 possible classes: standing person, unknown posture, motor bike, tourism car and utility car. Different types of classifiers (KNN and SVM with an RBF kernel) were used to examine the data. Accuracy rates above 92% have been achieved.