author = "Gon{\c{c}}alves, Gabriel Resende and Diniz, Matheus Alves and 
                         Laroca, Rayson and Menotti, David and Schwartz, William Robson",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais} and {Universidade Federal do Paran{\'a}} and 
                         {Universidade Federal do Paran{\'a}} and {Universidade Federal de 
                         Minas Gerais}",
                title = "Real-time Automatic License Plate Recognition Through Deep 
                         Multi-Task Networks",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "automatic license plate recognition, deep learning, multi-task 
                         learning, traffic surveillance, real-time.",
             abstract = "With the increasing number of cameras available in the cities, 
                         video traffic analysis can provide useful insights for the 
                         transportation segment. One of such analysis is the Automatic 
                         License Plate Recognition (ALPR). Previous approaches divided this 
                         task into several cascaded subtasks, i.e., vehicle location, 
                         license plate detection, character segmentation and optical 
                         character recognition. However, since each task has its own 
                         accuracy, the error propagation between each subtask is 
                         detrimental to the final accuracy. Therefore, focusing on the 
                         reduction of error propagation, we propose a technique that is 
                         able to perform ALPR using only two deep networks, the first 
                         performs license plate detection (LPD) and the second performs 
                         license plate recognition (LPR). The latter does not execute 
                         explicit character segmentation, which reduces significantly the 
                         error propagation. As these deep networks need a large number of 
                         samples to converge, we develop new data augmentation techniques 
                         that allow them to reach their full potential as well as a new 
                         dataset to train and evaluate ALPR approaches. According to 
                         experimental results, our approach is able to achieve 
                         state-of-the-art results in the SSIG-SegPlate dataset, reaching 
                         improvements up to 1.4 percentage point when compared to the best 
                         baseline. Furthermore, the approach is also able to perform in 
                         real time even in scenarios where many plates are present at the 
                         same frame, reaching significantly higher frame rates when 
                         compared with previously proposed approaches.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "paper44.pdf",
        urlaccessdate = "2020, Dec. 04"