Motorcycle Detection and Tracking System with Occlusion Segmentation

This paper proposes a vision-based motorcycle monitoring system to detect and tracking motorcycles. The system proposes an occlusion detection and segmentation method. The method uses the visual length, visual width, and Pixel Ratio to detect the classes of the motorcycle occlusions and segment the motorcycle from each occlusive class. Because the motorcycle riders must put on their helmets, the helmet detection or search method is used to make sure whether the helmet/motorcycle exits or not. Experiments obtained by using complex road scenes are reported, which demonstrate the validity of the method in terms of robustness, accuracy, and time responses.