Smart Flow AI-driven Traffic Management System

Project Overview: Smart Flow is an AI-driven traffic management system that uses deep learning techniques to monitor real-time traffic, detect vehicles, and optimize traffic signals. This project leverages YOLOv3-tiny for vehicle detection, tracking, and counting. It aims to provide efficient traffic management at intersections by adjusting traffic signals based on real-time vehicle data.

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Features:

• Real-Time Vehicle Detection: Detects cars, trucks, buses, and motorcycles using the YOLOv3-tiny model.
• Vehicle Counting: Counts the number of vehicles detected in each lane.
• Dynamic Traffic Signal Management: Assigns green signals to lanes with the highest vehicle count, ensuring optimal traffic flow.
• Deadlock Prevention: Implements a mechanism to avoid deadlock situations by considering lanes that have waited for long periods.
• Prediction-Based Signal Assignment: Predicts and assigns green signals based on real-time vehicle counts.
• Vehicle Tracking: Continuously tracks vehicles with a bounding box, displaying their probability of being a vehicle.

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Technologies Used:

• Deep Learning: YOLOv3-tiny for vehicle detection.
• OpenCV: For video processing and displaying real-time results.
• MoviePy: For video frame extraction and parallel processing.
• Python: Programming language used for implementation.

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Installation Instructions:

1. Clone the Repository:

   git clone https://github.com/your-username/Smart-Flow-Traffic-Management.git
   cd Smart-Flow-Traffic-Management

2. Install Dependencies:

   • Install required Python packages using pip:

   pip install -r requirements.txt

3. Download YOLOv8 Model Weights:

   • Download the YOLOv8 model weights and place them in the project folder.

   wget https://path-to-yolov8-model/yolov8n.pt

4. Prepare Input Video:

   • Place your video file (e.g., rush.mp4) in the videos/ directory.

5. Run the System:

   • Run the Python script to start processing the video:

   python traffic_management.py

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How It Works:

1. Video Input: The system takes a video input of the traffic scene with multiple lanes.
2. Frame Extraction: The video is processed frame-by-frame to detect vehicles.
3. Vehicle Detection: YOLOv3-tiny is used to detect vehicles (cars, buses, trucks, and motorcycles).
4. Vehicle Tracking and Counting: Each detected vehicle is tracked and counted for each lane.
5. Traffic Signal Management: The lane with the highest vehicle count is assigned a green signal. The system dynamically updates signals based on vehicle counts.
6. Deadlock Prevention: If a lane has not received a green signal for a while, it is given priority to avoid deadlock.

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Future Scope:

• Criminal Tracking: Detect traffic rule violations and alert the nearest police station.
• Collision Detection: Predict and prevent potential traffic collisions.
• Number Plate Recognition: Integrate number plate recognition for vehicle identification.
• Emergency Vehicle Detection: Detect and prioritize emergency vehicles like ambulances and police cars.