A Review of Vision-Based Traffic Semantic Understanding in ITSs
Jing Chen, Qichao Wang, Harry H. Cheng, Weiming Peng, Wenqiang Xu
Abstract
A semantic understanding of road traffic can help people understand road traffic flow situations and emergencies more accurately and provide a more accurate basis for anomaly detection and traffic prediction. At present, the overview of computer vision in traffic mainly focuses on the static detection of vehicles and pedestrians. There are few in-depth studies on the semantic understanding of road traffic using visual methods. This paper aims to review recent approaches to the semantic understanding of road traffic using vision sensors to bridge this gap. First, this paper classifies all kinds of traffic monitoring analysis methods from the two perspectives of macro traffic flow and micro road behavior. Next, the techniques for each class of methods are reviewed and discussed in detail. Finally, we analyze the existing traffic monitoring challenges and corresponding solutions.