  In many Asian countries with unconstrained road traffic conditions, driving
violations such as not wearing helmets and triple-riding are a significant
source of fatalities involving motorcycles. Identifying and penalizing such
riders is vital in curbing road accidents and improving citizens' safety. With
this motivation, we propose an approach for detecting, tracking, and counting
motorcycle riding violations in videos taken from a vehicle-mounted dashboard
camera. We employ a curriculum learning-based object detector to better tackle
challenging scenarios such as occlusions. We introduce a novel trapezium-shaped
object boundary representation to increase robustness and tackle the
rider-motorcycle association. We also introduce an amodal regressor that
generates bounding boxes for the occluded riders. Experimental results on a
large-scale unconstrained driving dataset demonstrate the superiority of our
approach compared to existing approaches and other ablative variants.
