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

TL;DR

This paper presents a comprehensive system for detecting, tracking, and counting motorcycle rider violations in unconstrained road videos, utilizing novel object detection and occlusion handling techniques to improve accuracy in challenging scenarios.

Contribution

It introduces a curriculum learning-based detector, a trapezium-shaped boundary representation, and an amodal regressor for robust violation detection and rider-motorcycle association.

Findings

Outperforms existing methods on large-scale datasets

Effective in occlusion and challenging traffic scenarios

Improves rider violation detection accuracy

Abstract

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.