A Review on Advanced Detection Methods in Vehicle Traffic Scenes

In an age that is changing over time, vision-based technology is also constantly evolving. Object detection plays a vital role in these trends. This paper provides review information on vehicle detection methods used for vehicle traffic monitoring system. The detection methods are analyzed by extracting different features techniques which are mainly for multi-scale vehicle objects and various environment conditions. In rapid growth of deep learning methods for vehicle detection task, it is focused on region-based methods such as R-CNN (Region-based Convolutional Neural Network) and regression-based methods such as YOLO (You Only Look Once) and also presented each improved versions. In this survey, the performance analysis for detection methods are depicted with experimental results using benchmark datasets in measuring accuracy. This review paper is aimed at the improvement of advanced deep learning frameworks for vehicle detection in real-time.