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In many Asian countries with unconstrained road traffic conditions, drivingviolations such as not wearing helmets and triple-riding are a significantsource of fatalities involving motorcycles. Identifying and penalizing suchriders is vital in curbing road accidents and improving citizens' safety. Withthis motivation, we propose an approach for detecting, tracking, and countingmotorcycle riding violations in videos taken from a vehicle-mounted dashboardcamera. We employ a curriculum learning-based object detector to better tacklechallenging scenarios such as occlusions. We introduce a novel trapezium-shapedobject boundary representation to increase robustness and tackle therider-motorcycle association. We also introduce an amodal regressor thatgenerates bounding boxes for the occluded riders. Experimental results on alarge-scale unconstrained driving dataset demonstrate the superiority of ourapproach compared to existing approaches and other ablative variants.

Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads