A Survey: Vehicle Detection and Counting

Controlling the flow of traffic requires an advance traffic management to know what is happening on the busiest urban junctions. As there are billions of traffic cameras around the world that act as sensors, it is quite easy to collect real-time data about traffic flows, but then using that data for processing and controlling traffic flows is another challenge. For the detection of vehicles, there are several outdated techniques, such as the use of an inductive loop detector, infrared, laser sensors, etc. Numerous deep learning techniques and computer vision techniques are currently being experimented with worldwide by researchers. The purpose of this paper is to review and compare deep learning- and computer vision-based systems for detecting and counting vehicles for various video-based surveillance applications. In reviewing the works of various researchers, we considered the approaches, accuracy, and results upward, as well as the future difficulties of this field of research.