A review of object detection approaches for traffic surveillance systems

Abstract

With the decreasing cost of traffic cameras and rapid advancement in computer vision and artificial intelligence, developing robust traffic surveillance systems has become more feasible and practical. These systems can easily outperform traditional human monitoring systems, as they can collect and analyze traffic data coming from multiple cameras efficiently. A good understanding of this data allows the detection easily road anomalies in real time and in an autonomous way. Therefore, an intelligent traffic system typically consists of three components: object detection, object tracking, and behavior analysis components. In this paper, we present a review of some of the well-known object detection techniques used in traffic video surveillance. The review begins with a brief introduction to the history of object detection and the evolution of its techniques. Then we review separately the two main approaches of detection, which are traditional and deep learning approaches of detection. Finally, an experimental analysis has been conducted to evaluate and compare the performance of some of the recent relevant detection methods in terms of speed and precision, in detecting vehicles in a traffic scenario.