Multi-traffic Scene Perception Model using Different Machine Learning Classifiers

Abstract

Traffic accidents are particularly serious on a rainy day, a dark night, an overcast and/or rainy
night, a foggy day, and many other times with low visibility conditions. Present vision driver
assistance systems are designed to perform under good-natured weather conditions. Classification
is a methodology to identify the type of optical characteristics for vision enhancement algorithms
to make them more efficient. To improve machine vision in bad weather situations, a multi-class
weather classification method is presented based on multiple weather features and supervised
learning. First, underlying visual features are extracted from multi-traffic scene images, and then
the feature was expressed as an eight-dimensions feature matrix. Second, five supervised learning
algorithms are used to train classifiers. The analysis shows that extracted features can accurately
describe the image semantics, and the classifiers have high recognition accuracy rate and adaptive
ability. The proposed method provides the basis for further enhancing the detection of anterior
vehicle detection during nighttime illumination changes, as well as enhancing the driver’s field of
vision on a foggy day