Traffic Violation Detection System
An AI-powered real-time traffic violation detection system that uses a live camera feed to automatically identify vehicle violations including helmet violations, red-light running, lane violations, overspeeding, and wrong-side driving.
Pilot selected for deployment at 3 high-traffic intersections (Q2 2026)** — with city-wide rollout potential.
What It Does
The system connects to a camera feed and uses Computer Vision to detect traffic violations in real time — no human monitoring required. When a violation is detected, it logs the event, timestamps it, and flags it for action.
Violations detected:** No helmet (two-wheeler riders) Red-light running Lane violation Overspeeding Wrong-side driving Illegal parking
Performance
| Metric | Result |
|---|---|
| Detection Accuracy | 89% |
| Processing Speed | 15 FPS real-time |
| False Positive Reduction | 35% (via data augmentation) |
| Training Dataset | 5,000+ annotated images |
| Labor Cost Savings | ₹90L/year (manual surveillance replaced) |
| Intersections monitored | 10+ simultaneously |
Tech Stack
| Component | Technology |
|---|---|
| Core Model | CNN (Convolutional Neural Network) |
| Vision Library | OpenCV |
| Language | Python |
| Training Framework | TensorFlow / Keras |
| Data Processing | NumPy, Pandas |
| Annotation | Custom labeled dataset (5,000+ images) |
Getting Started
pip install opencv-python tensorflow keras numpy pandaspython traffic_detection_single.pyMake sure your camera is connected. The system will start reading the feed and detecting violations in real time.
traffic-violation-detection/
│
├── traffic_detection_single.py # Main detection script (camera input → violation output)
├── traffic_violation_detection_system.html # Dashboard UI for monitoring & analytics
└── README.md
The included HTML dashboard (traffic_violation_detection_system.html) provides:
- Live camera feed simulation across 6 intersections
- Real-time violation analytics charts
- Challan / fine issuing interface
- Location-based camera monitoring
- Hourly violation trend graphs
Open in any browser — no server needed.
This project was identified as a pilot deployment candidate for 3 high-traffic intersections, with a city-wide rollout budget of ₹8Cr. The system replaces manual surveillance, enabling:
- 60% reduction in manual monitoring labor
- ₹90L annual savings in surveillance costs
- Automated, consistent enforcement across 10+ intersections
This project was part of the work that contributed to:
- 2nd Place — TEXPO 2K24 Innovation Pitch Competition
Selva Ganesh K
AI Research Analyst & Technical Writer
B.Tech — Artificial Intelligence & Machine Learning
- 🌐 Blog: mysticquill.blogspot.com
- 💼 LinkedIn: linkedin.com/in/selva-ganesh-61344b342
- 📧 selvaganeshtarun1423@gmail.com
This project is for educational and demonstration purposes.