Development of an AI-Driven Traffic Monitoring System for Enhanced Road Safety and Compliance

The client faces difficulties in accurately detecting traffic violations such as seat belt violations, distracted driving behaviors, and vehicle anomalies across diverse environmental conditions. Current manual or semi-automated systems lack sufficient detection accuracy, real-time processing capabilities, and scalability to meet expanding traffic safety mandates and regulatory compliance across multiple jurisdictions.

A mid-sized municipal transportation agency seeking to modernize their traffic enforcement and monitoring capabilities using AI and computer vision technologies.

• Develop a robust computer vision-based system capable of automatically detecting seat belt violations, distracted driving behaviors, and vehicle anomalies with high accuracy (~88-91%).
• Create a real-time processing platform supporting live video streams at 30 FPS for immediate violation detection and enforcement actions.
• Design a scalable and adaptable system capable of handling diverse environmental conditions, such as varying lighting, weather, and camera angles.
• Enable integration with vehicle license plate recognition systems for automated issuing of violations and fines.
• Validate the system’s performance across day and night scenarios to ensure consistent high detection accuracy.

• Automated detection of seat belt fastening status and violations
• Detection of distracted driving behaviors such as talking or texting on phones, eating, or smoking
• Vehicle detection with attributes such as plate number, speed, color, and size
• Support for real-time video stream analysis at 30 FPS
• Automated license plate recognition (ALPR) integration
• Violation alert generation and dispatch integration for enforcement

• Vehicle license plate recognition (ALPR) systems
• Notification and dispatch systems for enforcement alerts
• Existing traffic camera infrastructure

• Detection accuracy of at least 88% for seat belt violations and 91% for distracted driving detection
• Realtime streaming support at 30 FPS with minimal latency
• System must operate effectively under varying environmental conditions (day/night, weather effects)
• Scalable architecture supporting future expansion and additional data sources
• Secure data handling and compliance with privacy regulations

The implementation of this AI-powered traffic management system is projected to significantly enhance road safety by increasing violation detection accuracy (~88-91%), supporting real-time enforcement, and reducing manual oversight. It will enable the client to expand their market reach, improve compliance, and foster safer urban transportation environments, ultimately leading to higher enforcement efficiency and reduced accident rates.