Detect littering, unsafe driving, and public violations in real-time using computer vision. Built for smart cities, campuses, and enforcement systems.
A deterministic pipeline designed for field reliability and auditability, not black-box magic.
Real-time video streams from roadside or surveillance cameras.
Vehicles, people, and relevant objects are detected using trained CV models.
Actions like litter throwing and rash movement patterns are identified over time.
OCR extraction maps events to vehicle identity for traceable enforcement.
Human or AI-assisted validation happens before issuing a legal action.
Violation reports are generated for integrated enforcement and dashboard systems.
People trust AI when they can see it working. Upload a clip, simulate detection, and monitor live events from the same screen.
For CCTV integrations use HTTPS HLS or MP4 gateway URLs. Raw RTSP links are not directly playable in browsers.
Ready
| Event ID | Status | Plate | Violation | Confidence | Actions |
|---|
Monitor littering and public behavior at scale with auditable workflows.
Detect unsafe driving patterns and improve traffic risk visibility.
Automate cleanliness and compliance operations with less manpower.
Integrate with existing camera infrastructure for fast rollout.
Indicative pricing: INR 500 to INR 2000 per camera per month.
Built after participating in multiple AI hackathons where it became clear that AI can solve civic problems beyond chatbot experiences. Littering and traffic violations are everywhere, while enforcement remains weak. This system is focused on practical automation with accountability.
Every action can be reviewed before any fine is issued.
Data handling is designed for minimum exposure and operational necessity.
No facial recognition is used without explicit legal and consent frameworks.
Designed for authorized civic and enforcement use cases only.
Repositioned from single-use litter detection to an extensible AI Traffic Intelligence System.