A Real Time approach for detecting and counting Vehicles using OpenCV and NumPy

Vehicle detection and counting using OpenCV is a crucial aspect of modern traffic management systems. This mini project aims to develop a robust solution for detecting vehicles in real-time video streams and accurately counting them as they pass through a designated area. OpenCV, a popular computer vision library, is utilized for its extensive functionality and ease of implementation. First, the video stream is captured and processed to extract individual frames. Subsequently, each frame undergoes preprocessing techniques such as resizing, grayscale conversion, and noise reduction to enhance vehicle detection accuracy. Vehicle detection is achieved through the implementation of a deep learning- based object detection algorithm, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector). These algorithms are trained on large datasets to recognize vehicles with high precision. Once vehicles are detected, a tracking algorithm is employed to maintain continuity between frames and accurately count the number of vehicles passing through the specified region of interest. This tracking algorithm ensures that vehicles are not double-counted and provides a reliable count for traffic analysis purposes.