Traffic Violations using CCTV
Most cities are already covered with CCTV networks — yet these cameras merely record incidents rather than interpret them.
Manual review of hours of footage is time-consuming, inconsistent, and often misses key violations.
Municipal enforcement agencies struggle with:
- Detecting signal jumps, helmet non-usage, and seat-belt violations at scale
- Lack of automated evidence packets linked to e-challan systems
- Human dependency for reviewing each event
- High false-positive rates leading to citizen disputes and credibility loss
The result: millions of traffic violations go unrecorded every month, and enforcement teams remain reactive rather than preventive.
NAYAN converts existing CCTV infrastructure into a smart traffic enforcement grid powered by real-time computer vision.
Our system ingests video streams from junction cameras and applies detection models for:
- Red-light jump and stop-line violation
- Helmet / seat-belt / mobile-phone usage
- Triple-riding and wrong-lane movement
- Lane discipline and no-parking detection
Each event is time-stamped, geo-tagged, and paired with vehicle imagery. Alerts are transmitted to enforcement dashboards within seconds — along with metadata (camera ID, violation type, confidence score).
Supervisors can review, verify, and auto-export verified events to existing e-challan or law-enforcement databases.
This drastically minimizes human review effort while improving evidence quality and transparency.
Enterprise ready deployments
Percentage Coverage of Cities increase from 0.1% Due to CCTV’s to 50% Due to AI Dashcams
Washington
California
Huntington
NAYAN’s platform can detect violations including red-light jumps, stop-line violations, helmet non-usage, mobile-phone usage by drivers/riders, wrong-way driving, triple-riding, illegal lane change or wrong-lane movement, and no-parking / lane-discipline infringements. nayan.co+1
For every detected event the system captures evidence in the form of a video clip or image snapshot along with metadata such as timestamp, GPS or camera-ID, violation type, and confidence score. This evidence packet is ready for forwarding to e-challan systems or law-enforcement databases. nayan.co
Much less than traditional systems. Because detections are automated and evidence enriched with metadata, only rapid verification may be required by supervisors—substantially reducing human review time. nayan.co
Yes. The platform is designed to work with existing city camera networks and supports integration with municipal command centres and enforcement back-ends so that validated violation events can be exported for e-challan generation or compliance workflows. nayan.co+1
Typical outcomes include dramatic reductions in case-processing time, significant drops in manual review workload, high detection accuracy (e.g., > 97% in certain deployments) and an increase in revenue recovery from fines due to improved detection and faster processing. nayan.co
Yes — the system is designed for large-scale deployments, handling many feeds in real time, and is optimised for dense urban environments where many violations might otherwise go undetected.