A Robust Approach for Road Users Classification Using Motion Cues

This thesis presents a framework that is designed to classify road users into vehicles, cyclists and pedestrians by using the motion cues obtained from their tracks. The road user tracks are obtained using a tracker system such as computer vision techniques. The separate pieces of information are gained from these motion cues are hereafter called Classifiers. There are nineteen classifiers included in this framework. After obtaining the classifiers’ values from the tracked objects’ tracks, the information from these classifiers will be assessed and integrated using fuzzy membership approach, which in turn requires prior configurations to be available. This will lead to the final classification. The performance of this framework demonstrated very promising results under different measures. An important contribution of this study is the creation of a robust approach that can integrate different motion cues using fuzzy membership framework.