1. ABHIJEET KUMAR
2. SWATI MISHRA
3. SAYAK HATUI
4. ANIRUDDHA MUKHERJEE
5. AAKRITI ROY
Enhancing Road Safety with AI/ML for Traffic Flow Optimization.
Optimizing traffic flow using computer vision, machine learning, and deep learning to analyze traffic camera footage and provide real-time traffic condition suggestions.
The project aims to enhance traffic management by offering advanced notifications about traffic conditions and hazards like potholes, empowering informed decision-making for accident avoidance. Key features include:
- People Counting: Utilizing computer vision algorithms to count pedestrians, aiding in optimizing pedestrian crossings.
- Pothole Detection: Identifying road defects through image segmentation for preventive maintenance.
- Traffic Light Detection: Detects traffic lights and the signal shown(red/yellow/green) displayed by it.
- Vehicle Speed Detection: Estimating vehicle speeds to identify traffic congestion points, aiding in traffic light signal adjustments or diversions.
- Lane-Specific Vehicle Counting: Differentiating vehicles in lanes to manage flow at bottlenecks or adjust lane usage dynamically.
- Lane-Wise Vehicle Tracking: This feature flags wrong-way vehicles, counts traffic, detects vehicle types, prevents overcrowding.
The system continuously processes video feeds from traffic cameras, providing up-to-date traffic information.
This approach demonstrates the application of AI and machine learning in improving traffic flow and road safety through advanced data analysis and real-time monitoring.