Navigating the Road: A Thorough Analysis of Vehicle Detection Methods in Current Computer Vision

Vehicle detection, a fundamental problem in computer vision, still faces great challenges and has wide applications such as traffic surveillance system, autonomous driving vehicle and safe city etc. In the past few years many methods are proposed to improve accuracy and efficiency of vehicle detection in images, videos. The review paper provides a comprehensive review of these methods, and their development from traditional techniques like background subtraction to more complex deep learning approaches such as Single Shot Multibox Detector (SSD), You Only Look Once (YOLO), Histograms of Oriented Gradient(HOG)-based algorithms, Haar Cascade Classifiers, Faster R-CNN. We examine these methods across a wide array of datasets, characterizing the relative strengths and weaknesses in an effort to provide our views on what is likely optimal today. Additionally, we present recommendations for future research directions in vehicle detection, hoping to help both industry experts and university academics. All things considered, this study is a great tool for learning about and developing the area of vehicle detection.