author = "Santos, Adson M. and Bastos-Filho, Carmelo J. A. and Maciel, 
                         Alexandre M. A. and Lima, Estanislau",
          affiliation = "{University of Pernambuco} and {University of Pernambuco} and 
                         {University of Pernambuco} and {University of Pernambuco}",
                title = "Counting Vehicle with High-Precision in Brazilian Roads Using 
                         YOLOv3 and Deep SORT",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Computer Vision, Vehicle Count, Traffic Monitoring System, Object 
                         Detection, Multiple Object Tracking.",
             abstract = "The Brazilian National Department of Transport Infrastructure 
                         (DNIT) maintains the National Traffic Counting Plan (PNCT). The 
                         main goal of PNCT is to evaluate the current flow of traffic on 
                         federal highways aiming to define public policies. However, DNIT 
                         still performs the quantitative classificatory surveys not 
                         automated or with invasive equipment. It is crucial for conducting 
                         traffic studies to search for more modern solutions to accomplish 
                         a higher number of automated non-invasive, and low-cost 
                         classificatory surveys. This paper proposes a system that uses 
                         YOLOv3 for object detection and the Deep SORT for multiple objects 
                         tracking algorithms. From the results over real-world videos 
                         collected in Brazilian roads, we obtained a precision above 90% in 
                         the global vehicle count. We also show that our proposal 
                         outperformed other previously proposed tools with 99.15% precision 
                         in public datasets. We believe this paper's proposal allows the 
                         development of a traffic analysis tool to be used for the 
                         automation of the volumetric traffic surveys, enabling to improve 
                         the DNIT agility and generating economy for the public coffers.",
  conference-location = "Virtual",
      conference-year = "Nov. 7-10, 2020",
             language = "en",
           targetfile = "Article Sibgrapi_2020___Counting Vehicle with 
        urlaccessdate = "2021, Jan. 19"