The Smart Traffic Monitoring and Prediction System is a Python-based project that analyzes traffic conditions using pre-recorded video footage. It employs YOLOv8 for vehicle detection, calculates traffic density, predicts congestion levels 2 seconds ahead using statistical and machine learning methods, and presents results in a web dashboard powered by Flask. This system serves as a practical demonstration of computer vision and machine learning for traffic monitoring.
- Detects vehicles (cars, buses, trucks, motorcycles) using YOLOv8 with confidence scores.
- Calculates traffic density based on the full video frame, scaled to vehicles per million pixels.
- Predicts congestion using a moving average (dashboard) and linear regression (separate script).
- Displays results in a user-friendly web interface with annotated video frames.
- Python 3.8 or higher
- Required libraries:
opencv-python,ultralytics,torch,torchvision,numpy,pandas,flask,scikit-learn
- Clone the Repository:
git clone https://github.com/Lin172005/Smart-Traffic-Monitoring-and-Prediction-System.git cd Smart-Traffic-Monitoring-and-Prediction-System