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• W4285220706 endingPage "19979" @default.
• W4285220706 startingPage "19954" @default.
• W4285220706 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." @default.
• W4285220706 created "2022-07-14" @default.
• W4285220706 creator A5011290020 @default.
• W4285220706 creator A5041736766 @default.
• W4285220706 creator A5055739393 @default.
• W4285220706 creator A5059602960 @default.
• W4285220706 creator A5075029986 @default.
• W4285220706 date "2022-11-01" @default.
• W4285220706 modified "2023-10-11" @default.
• W4285220706 title "A Review of Vision-Based Traffic Semantic Understanding in ITSs" @default.
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