Integrated Image Processing Pipeline for Improved Vehicle Identification and Counting in Video Streams

This study introduces a proposed integrated image processing pipeline to enhance vehicle detection and counting precision in real-time video streams. This method can accurately pinpoint areas in the videos where cars are present by using Regions of Interest (ROIs) and segmenting the frames. To ensure efficient processing of images, it is essential to optimize their quality and dimensions. Utilizing Convolutional Neural Networks (CNNs) for feature extraction enables us to impart discriminative features through hierarchical layers. Afterward, machine learning models enhance the extracted features before applying them to classify automobiles. There are two post-processing tasks: implementing vehicle counting methods that consider discovered ROIs and optimizing image size further. The primary aim is to achieve precise and effective vehicle counting and identification with this method, which is essential for tasks like traffic surveillance. Based on the experimental findings, the system effectively balances processing efficiency and accuracy in vehicle recognition and classification. With this integrated infrastructure, real-time enhancements could be made to processing traffic surveillance video streams.