Overtaking & Receding Vehicle Detection for Driver Assistance and Naturalistic Driving Studies

Although on-road vehicle detection is a well-researched area, overtaking and receding vehicle detection with respect to (w.r.t) the ego-vehicle is less addressed. In this paper, we present a novel appearance-based method for detecting both overtaking and receding vehicles w.r.t the ego-vehicle. The proposed method is based on Haar-like features that are classified using Adaboost-cascaded classifiers, which result in detection windows that are tracked in two directions temporally to detect overtaking and receding vehicles. A detailed and novel evaluation method is presented with 51 overtaking and receding events occurring in 27000 video frames. Additionally, an analysis of the detected events is presented, specifically for naturalistic driving studies (NDS) to characterize the overtaking and receding events during a drive. To the best knowledgea of the authors, this automated analysis of overtaking/receding events for NDS is a first of its kind in literature.