Published Paper Details:

Vehicle counting, classification, and detection have become essential tasks in modern transportation systems and surveillance applications. This abstract presents a comprehensive overview of these critical functions using OpenCV, a popular computer vision library. Vehicle counting involves tracking and tallying the number of vehicles passing through a specific region. OpenCV provides tools for frame differencing, contour detection, and object tracking, allowing for real-time and accurate vehicle counting in various scenarios. Classification goes a step further by categorizing vehicles into different classes, such as cars, trucks, or motorcycles. OpenCV can leverage deep learning models like Convolutional Neural Networks (CNNs) to achieve high-precision vehicle classification based on image features. Vehicle detection, on the other hand, identifies and localizes vehicles within an image or video stream. OpenCV offers pre-trained models like Haar Cascades and YOLO(You Only Look Once) for robust vehicle detection.