Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads
This addresses road safety issues in Asian countries by automating violation detection, though it is incremental with novel components for specific challenges.
The paper tackled the problem of detecting, tracking, and counting motorcycle traffic violations like not wearing helmets and triple-riding in unconstrained road videos, achieving superior results compared to existing methods on a large-scale dataset.
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.