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Traffic Monitoring and Violation Detection Using Edge AI

We developed an AI-powered traffic monitoring system to help government agencies and smart-city departments detect violations, analyze flow, and ensure road safety in real time.
 By combining computer vision, Edge AI, and custom-trained models, the system delivers actionable traffic insights directly from on-site cameras.

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Summary :

We have built an Edge AI–driven traffic monitoring solution capable of detecting vehicles, speed violations, parking issues, and traffic anomalies in real time. Using computer vision and deep learning, it processes live camera feeds at intersections to identify events automatically and trigger alerts. This scalable system helps authorities improve response times, enforce compliance, and enhance safety with data-driven insights.

Problem

Problem

Objectives

  • Enable Real-Time Detection ,

    Automate vehicle tracking and violation detection using AI and computer vision.

  • Improve Road Safety

    Identify overspeeding, lane violations, and traffic anomalies accurately.

  • Optimize Edge Processing

    Run models on edge devices for instant response and reduced latency.

  • Reduce Manual Intervention

    Replace human-based observation with automated analytics.

  • Provide Actionable Insights

    Deliver real-time dashboards and reports for traffic management teams.

Challenges

  • Maintaining accurate detection in varied lighting and weather conditions.
  • Processing multiple video streams simultaneously at edge hardware level.
  • Optimizing model inference speed without compromising accuracy.
  • Handling diverse vehicle types and camera angles across locations.
  • Ensuring stable system performance under continuous 24/7 operations.

Solution

  • Developed a computer vision–based traffic monitoring system using custom-trained YOLOv8 models for multi-vehicle detection.
  • Deployed the solution on NVIDIA Jetson edge devices to achieve low-latency, high-throughput performance.
  • Integrated TensorRT optimization and OpenVINO acceleration for real-time model inference.
  • Created a custom dataset combining automated and manual labeling for improved regional accuracy.
  • Designed an interactive QT C++ dashboard to visualize live traffic data, violations, and event logs.
  • Enabled speed violation and anomaly detection through dynamic analysis of frame sequences.
  • Delivered a robust system capable of monitoring multiple lanes, intersections, and parking zones simultaneously.

Technologies

Architecture

Video Capture
Cameras capture live traffic footage from intersections and highways.
Edge Processing
AI models on Jetson devices detect vehicles and analyze motion patterns.
Violation Detection
System identifies overspeeding, parking violations, and anomalies.
Data Aggregation
Detected events are logged and transmitted securely.
Visualization
QT-based dashboard displays live metrics and alert summaries.

Results & Impact

Enabled real-time traffic violation detection with instant alerts

Enhanced decision-making through data-driven dashboards

Reduced manual monitoring time and improved accuracy

Optimized performance on edge devices, lowering latency and cost

Improved public safety through consistent and scalable enforcement

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