@inproceedings{Goncalves:2019:CIARP,
title = {Multi-Task Learning for Low-Resolution License Plate Recognition},
author = {Gabriel Resende Goncalves and Matheus Alves Diniz and Rayson Laroca and David Menotti and William Robson Schwartz},
url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_CIARP_Goncalves.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)},
pages = {1-10},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Goncalves:2018:SIBGRAPI,
title = {Real-time Automatic License Plate Recognition Through Deep Multi-Task Networks},
author = {Gabriel Resende Gonçalves and Matheus Alves Diniz and Rayson Laroca and David Menotti and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper.pdf},
year = {2018},
date = {2018-09-04},
booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)},
pages = {1-8},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Bastos:2018:AVSS,
title = {MORA: A Generative Approach to Extract Spatiotemporal Information Applied to Gesture Recognition},
author = {Igor Leonardo Oliveira Bastos and Victor Hugo Cunha de Melo and Gabriel Resende Gonçalves and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/MORA_.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {15th International Conference on Advanced Video and Signal-based Surveillance (AVSS)},
pages = {1-6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Goncalves:2016:ITSC,
title = {License Plate Recognition based on Temporal Redundancy},
author = {Gabriel Resende Gonçalves and David Menotti and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_ITSC.pdf},
year = {2016},
date = {2016-11-04},
booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)},
pages = {1-5},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{2016:JEI:Gabriel,
title = {Benchmark for License Plate Character Segmentation},
author = {Gabriel Resende Gonçalves and Sirlene Pio Gomes da Silva and David Menotti and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/JEI-2016-Benchmark.pdf},
isbn = {1017-9909},
year = {2016},
date = {2016-10-24},
journal = {Journal of Electronic Imaging},
volume = {25},
number = {5},
pages = {1-5},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@mastersthesis{Goncalves:2016:MSc,
title = {License Plate Recognition based on Temporal Redundancy},
author = {Gabriel Resende Goncalves},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/dissertation_2016_Gabriel.pdf},
year = {2016},
date = {2016-08-26},
school = {Federal University of Minas Gerais},
abstract = {Recognition of vehicle license plates is an important task applied to 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 the characters 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 techniques might have their recognition rates reduced due to noise present in that particular frame. On the other hand, in this work we propose an approach to automatically detect the vehicle on the road and identify (locate/recognize) its license plate based on temporal redundant information instead of selecting a single frame to perform the recognition. We also propose two post-processing steps that can be employed to improve the accuracy of the system by querying a license plate database (e.g., the Department of Motor Vehicles database containing a list of all issued license plates and car models). Experimental results demonstrate that it is possible to improve the vehicle recognition rate in 15.5 percentage points (p.p.) (an increase of 23.38%) of the baseline results, using our proposal temporal redundancy approach. Furthermore, additional 7.8 p.p. are achieved using the two post-processing approaches, leading to a final recognition rate of 89.6% on a dataset with 5,200 frame images of $300$ vehicles recorded at Federal University of Minas Gerais (UFMG). In addition, this work also proposes a novel benchmark, designed specifically to evaluate character segmentation techniques, composed of a dataset of 2,000 Brazilian license plates (resulting in 14,000 alphanumeric symbols) and an evaluation protocol considering a novel evaluation measure, the Jaccard-Centroid coefficient.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}

@inproceedings{Peixoto:2015:WVC,
title = {Brazilian License Plate Character Recognition using Deep Learning},
author = {Sirlene Peixoto and Gabriel Resende Gonçalves and Guillermo Camara-Chavez and William Robson Schwartz and David Menotti Gomes},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_WVC_Peixoto.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {Workshop em Visao Computacional (WVC)},
pages = {1-5},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}