Traffi: Smart Surveillance for Roads

Vehicle detection and counting play a crucial role in intelligent transportation systems, traffic monitoring, and urban planning. Traditional methods for vehicle detection often struggle with accuracy and real-time performance, especially in dynamic environments. With the advancement of deep learning, object detection models like YOLOv4 (You Only Look Once) have significantly improved detection speed and accuracy. Coupled with OpenCV, a powerful computer vision library, YOLOv4 enables efficient vehicle detection and tracking in real-world scenarios. In this work, we implement a vehicle detection and counting system using the YOLOv4 deep learning model and OpenCV. The system processes video streams to detect vehicles, classify them, and count their movement across predefined regions. YOLOv4's convolutional neural network architecture allows for high-speed inference, while OpenCV handles image preprocessing, post-processing, and visualization. The model is trained on a dataset of various vehicle types and optimized for real-time performance on both CPU and GPU environments. Our implementation achieves high accuracy in vehicle detection and counting, even in challenging conditions such as occlusions, varying lighting, and heavy traffic. The system demonstrates real-time processing capabilities, making it suitable for smart traffic management applications. By leveraging YOLOv4 and OpenCV, we provide a robust and efficient solution for automated vehicle monitoring, contributing to improved traffic flow analysis and transportation planning.