This project leverages AI/ML technologies, including computer vision, machine learning, and deep learning, to optimize traffic flow and enhance road safety by analyzing real-time traffic camera footage. It aims to provide advanced notifications about traffic conditions and hazards, empowering informed decision-making and accident prevention.
- Optimize Traffic Flow: Analyze traffic camera footage in real-time to improve traffic management and safety.
- Enhance Road Safety: Provide timely notifications about hazards like potholes for accident prevention.
- People Counting: Employ computer vision to count pedestrians, optimizing pedestrian crossing times.
- Pothole Detection: Use image segmentation to identify road defects for timely maintenance.
- Traffic Light Detection: Detect traffic lights and their current signals (red/yellow/green) to inform drivers.
- Vehicle Speed Detection: Estimate vehicle speeds to identify and mitigate traffic congestion.
- Lane-Specific Vehicle Counting: Differentiate vehicles by lane to manage traffic flow at bottlenecks.
- Lane-Wise Vehicle Tracking: Track vehicles by lane for wrong-way vehicle detection, traffic counting, vehicle type identification, and overcrowding prevention.
The system processes video feeds from traffic cameras continuously, ensuring traffic information is always current and accurate. By applying sophisticated AI/ML algorithms, the project demonstrates the potential of technology to significantly improve traffic flow and road safety.
This project highlights the impactful application of AI and ML in traffic management and road safety. Through advanced data analysis and real-time monitoring, it aims to make roads safer and improve traffic flow, showcasing the power of technology in solving real-world problems.