%0 Conference Proceedings
%T Counting Vehicle with High-Precision in Brazilian Roads Using YOLOv3 and Deep SORT
%D 2020
%8 Nov. 7-10, 2020
%A Santos, Adson M.,
%A Bastos-Filho, Carmelo J. A.,
%A Maciel, Alexandre M. A.,
%A Lima, Estanislau,
%@affiliation University of Pernambuco
%@affiliation University of Pernambuco
%@affiliation University of Pernambuco
%@affiliation University of Pernambuco
%E Musse, Soraia Raupp,
%E Cesar Junior, Roberto Marcondes,
%E Pelechano, Nuria,
%E Wang, Zhangyang (Atlas),
%B Conference on Graphics, Patterns and Images, 33 (SIBGRAPI)
%C Virtual
%S Proceedings
%I IEEE Computer Society
%J Los Alamitos
%K Computer Vision, Vehicle Count, Traffic Monitoring System, Object Detection, Multiple Object Tracking.
%X 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.
%@language en
%3 Article Sibgrapi_2020___Counting Vehicle with High-Precision_Final_CamaraReady.pdf