Helmet and Triple-Riding Violation Detection Using Urban CCTV Feeds

Objective: To develop an automated system for municipal traffic authorities to detect helmetless and triple-riding violations in real-time from city CCTV footage.

Indian cities face persistent challenges in road safety due to traffic violations such as riding without helmets and carrying more than two persons on two-wheelers. These violations contribute to injuries and fatalities, straining public health and enforcement resources. The issue directly aligns with the Ministry of Road Transport & Highways’ mandate and Sustainable Development Goal 9 (industry, innovation, and infrastructure).

The proposed solution leverages existing municipal CCTV infrastructure and computer vision techniques to automatically identify helmetless riders and triple-riding instances. The system will process real-time video feeds, flag violations, and generate actionable reports for traffic enforcement teams without manual monitoring.

Key features include: robust detection models trained on Indian traffic datasets, integration with live CCTV streams, violation event logging, geo-tagging, and a dashboard for authorities. A working prototype will demonstrate detection accuracy on sample video feeds and provide violation analytics.

Measurable impact involves reducing manual workload, timely violation identification, and improved data-driven enforcement. The solution is scalable to other cities and can integrate with larger traffic management platforms, enhancing road safety nationwide.