Smart Traffic Analysis, Monitoring, and Density Estimation System Using YOLOv8

Smart Traffic Analysis, Monitoring, and Density Estimation System Using YOLOv8 and DeepSORT, aims to provide an efficient and real-time solution for vehicle detection, tracking, and speed estimation in traffic surveillance. The system leverages YOLOv8 (You Only Look Once version 8) for high-precision object detection and integrates DeepSORT (Simple Online and Realtime Tracker with a Deep Association Metric) for multi-object tracking, ensuring accurate identification and movement tracking of vehicles across video frames. The YOLOv8 model is trained on large-scale datasets for object detection and efficiently identifies vehicles in real-time. The detected bounding boxes, confidence scores, and class labels are then passed to the DeepSORT tracking algorithm, which assigns a unique ID to each detected object and maintains its trajectory across frames. DeepSORT employs an advanced Kalman filter for motion prediction and a deep appearance descriptor to ensure robust re-identification even in complex traffic conditions. Additionally, the system integrates speed estimation by tracking the movement of each vehicle across frames, calculating the pixel displacement over time, and converting it into an approximate speed (km/h). This enables real-time monitoring of traffic flow and assists in identifying potential rule violations, such as over speeding. The system processes video streams efficiently using OpenCV, NumPy, and PyTorch, making it suitable for both offline video analysis and live surveillance feeds. The output video includes annotated bounding boxes, unique tracking IDs, and estimated vehicle speeds. This solution is highly scalable and can be deployed for traffic density estimation, congestion monitoring, and automated traffic law enforcement. By combining deep learning-based detection with robust tracking and speed estimation, this project offers a faultless, real-time, and automated approach to traffic analysis, helping urban planners and law enforcement agencies manage road safety effectively.