The project provides an AI-based traffic infraction detection that automates the process of detection of helmet violations, seatbelt abuse, smoke emission, or red-light jumping. It correlates violations and lane and signal context, identifies vehicles and plates, bundles evidence automatically, and facilitates a human-in-loop review process, and then sends validated cases to e-ticket.
Human surveillance is unable to support large-scale traffic flow, complicated intersections and frequent violations. Lack of officers at work, distraction, or blind spots causes them to ignore an incident, resulting in inconsistent enforcement and less deterrent effect on dangerous road behavior.
There is a high rate of helmet abuse, seatbelt failures, smoke, and red-light jumps that are very dangerous to drivers, passengers, and other people. The authorities are not able to promptly identify and handle this dangerous situation without automated systems.
Manual detection does not tend to provide any reliable visual evidence, like timestamps, signal states or context of lanes. The lack of evidence undermines enforcement, high rates of disputes, and the opportunity to pursue cases, using both e-ticket system and the legal process.
Questionable cases require human supervision, and the existing tools provide slow and disjointed workflow. It causes more extensive review times, backlogs, and delays in confirming violations, particularly in busy areas with thousands of events daily.
Without unified dashboards, trend reporting, and camera health monitoring, authorities cannot analyze violation hotspots, equipment issues, or enforcement performance. This absence of insights makes it harder to plan interventions, optimize policing, or adjust traffic policies.
The system identifies a lack of helmet, issues with seatbelts, smoke emissions, and jumps at the red lights. It combines both lane and signal-state context and associates vehicles and reads plates where available thereby cutting down significantly on the reliance on manual observation.
Every violation triggers an automated case package containing images, a short video clip, timestamps, lane details, signal state, weather or lighting meta, plate data, and confidence scores. This ensures consistent, enforcement-ready documentation for every incident.
A special review interface allows the officers to review, approve, or reject cases effectively. Challengable events are marked, approved cases are sent directly to the e-ticket system of the authority and citizens are informed according to the policy.
The platform identifies visible exhaust plumes and gives them levels of severity to classify pollution. Such detections could be associated with environmental enforcement processes, lowering air pollution in cities.
The system is hosted on a Kubernetes GPU cluster and MinIO storage, Kafka streams, secure API gateway, RBAC/ABAC, audit logging and disaster recovery. Hashing and watermarking maintain evidence integrity, and KPIs monitor accuracy, review time, and rate of issuing tickets.
| Layer / Component | Technology / Tools | Purpose |
|---|---|---|
| Video Ingest Stream | Kafka | Handles real-time video events, metadata ingestion, and distribution to GPU workers. |
| GPU Processing Workers | Python services, PyTorch/TensorRT | Run per-violation models (helmet, seatbelt, smoke, red-signal) and generate outputs in real time. |
| Signal State Integration | SCATS / ATCS / Traffic Controller APIs | Provide accurate signal-phase data for red-light violation detection. |
| Model Functions | CNNs/Transformers, Object Detection + Classification Pipelines | Detect violations, analyze lane/signal context, and identify smoke emission or safety lapses |
| Plate Recognition Module | OCR Engines (EasyOCR / PaddleOCR / custom LPR) | Extract vehicle plate numbers when visible to complete case information. |
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