A Survey on Vehicle Detection, Counting, and Classification

In traffic management, surveillance, and intelligent transportation systems, vehicle recognition and counting are vital jobs with many applications. The development of practical and successful approaches to handle the challenges of vehicle recognition and counting has advanced significantly over time. This review article offers a thorough summary of the most recent methods, difficulties, and applications in the area of vehicle identification and counting. The review looks at studies that offer several methodologies for counting vehicles, including tried-and-true methods like background subtraction, optical flow analysis, and edge recognition. It also looks into papers that use cutting-edge methods like multi-camera systems, sensor fusion, and deep learning-based object detection to precisely estimate the number of vehicles in various situations. The review analysis of papers that present cutting-edge methods for recognizing and classifying automobiles according to their characteristics, such as type, make, and model, focuses on vehicle classification. It covers studies that use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as well as more conventional feature-based techniques, such as color, texture, and shape descriptors, for robust and accurate vehicle classification.