@InProceedings{GonçalvesMenoSchw:2016:ApLiPl,
               author = "Gon{\c{c}}alves, Gabriel Resende and Menotti, David and Schwartz, 
                         William Robson",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         do Paran{\'a}} and {Universidade Federal de Minas Gerais}",
                title = "An Approach for License Plate Recognition Based on Temporal 
                         Redundancy",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "automatic license plate recognition, pattern recogni- tion, 
                         license plate character segmentation, benchmark.",
             abstract = "Recognition of vehicle license plates is an important task that 
                         can be applied for a myriad of real scenarios. Most approaches in 
                         the literature first detect an on-track vehicle, locate the 
                         license plate, perform a segmentation of its characters and then 
                         recognize them using an Optical Character Recognition (OCR) 
                         approach. However, these approaches focus on performing these 
                         tasks using only a single frame of each vehicle in the video. 
                         Therefore, such approaches might have their recognition rates 
                         reduced due to noise present in that particular frame. In this 
                         work we propose an approach to automatically detect the vehicle on 
                         the road and identify its license plate based on temporal 
                         redundant information. We also propose a post-processing step that 
                         can be employed to improve the accuracy of the system. 
                         Experimental results demonstrate that it is possible to improve 
                         the vehicle recognition rate in 15.5 percentage points using our 
                         proposal temporal redundancy approach. Furthermore, additional 7.8 
                         percentage points are achieved using the post-processing 
                         technique, leading to a final recognition rate of 89.6%. 
                         Furthermore, this work also proposes a novel benchmark composed of 
                         a dataset designed to focus specifically on the character 
                         segmentation step of the ALPR, a new evaluation measure and an 
                         evaluation protocol.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M9GRCE",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M9GRCE",
           targetfile = 
                         AnApproachForLicensePlateRecognitionBasedOnTemporalRedundancy.pdf",
        urlaccessdate = "2024, Apr. 29"
}