2018 |
Gonçalves, Gabriel Resende; Diniz, Matheus Alves; Laroca, Rayson; Menotti, David; Schwartz, William Robson Real-time Automatic License Plate Recognition Through Deep Multi-Task Networks Inproceedings In: Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2018. Links | BibTeX | Tags: Automatic License Plate Recognition, Deep Learning, DeepEyes, GigaFrames, Multi-Task Learning, Sense-ALPR @inproceedings{Goncalves:2018:SIBGRAPI, |
2016 |
Gonçalves, Gabriel Resende; Menotti, David; Schwartz, William Robson License Plate Recognition based on Temporal Redundancy Inproceedings In: IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 1-5, 2016. Links | BibTeX | Tags: Automatic License Plate Recognition, DeepEyes, GigaFrames @inproceedings{Goncalves:2016:ITSC, |
Gonçalves, Gabriel Resende; da Silva, Sirlene Pio Gomes; Menotti, David; Schwartz, William Robson Benchmark for License Plate Character Segmentation Journal Article In: Journal of Electronic Imaging, 25 (5), pp. 1-5, 2016, ISBN: 1017-9909. Links | BibTeX | Tags: Automatic License Plate Recognition, Benchmark, Character Segmentation, DeepEyes, Featured Publication, GigaFrames, Jaccard Coefficient, Novel Dataset, Sense SegPlate @article{2016:JEI:Gabriel, |
Goncalves, Gabriel Resende License Plate Recognition based on Temporal Redundancy Masters Thesis Federal University of Minas Gerais, 2016. Abstract | Links | BibTeX | Tags: Automatic License Plate Recognition, DeepEyes, GigaFrames @mastersthesis{Goncalves:2016:MSc,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. |
2015 |
Peixoto, Sirlene; Gonçalves, Gabriel Resende; Camara-Chavez, Guillermo; Schwartz, William Robson; Gomes, David Menotti Brazilian License Plate Character Recognition using Deep Learning Inproceedings In: Workshop em Visao Computacional (WVC), pp. 1-5, 2015. Links | BibTeX | Tags: Automatic License Plate Recognition, Deep Learning @inproceedings{Peixoto:2015:WVC, |
2014 |
de Prates, Raphael Felipe Carvalho; Camara-Chavez, G; Schwartz, William Robson; Gomes, D M An Adaptive Vehicle License Plate Detection at Higher Matching Degree Inproceedings In: Iberoamerican Congress on Pattern Recognition (CIARP), pp. 454-461, Springer International Publishing, 2014. Links | BibTeX | Tags: Automatic License Plate Recognition, DET, License Plate Detection @inproceedings{Prates:2014:CIARP, |
2013 |
de Prates, Raphael Felipe Carvalho; Camara-Chavez, G; Schwartz, William Robson; Menotti, D Brazilian License Plate Detection Using Histogram of Oriented Gradients and Sliding Windows Journal Article In: International Journal of Computer Science and Information Technology, 5 , pp. 39-52, 2013. Links | BibTeX | Tags: ARDOP, Automatic License Plate Recognition, HOG, License Plate Detection @article{Prates:2013:IJCSIT, |
2018 |
Gabriel Resende Gonçalves; Matheus Alves Diniz; Rayson Laroca; David Menotti; William Robson Schwartz Real-time Automatic License Plate Recognition Through Deep Multi-Task Networks Inproceedings In: Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2018. Links | BibTeX | Tags: Automatic License Plate Recognition, Deep Learning, DeepEyes, GigaFrames, Multi-Task Learning, Sense-ALPR @inproceedings{Goncalves:2018:SIBGRAPI, |
2016 |
Gabriel Resende Gonçalves; David Menotti; William Robson Schwartz License Plate Recognition based on Temporal Redundancy Inproceedings In: IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 1-5, 2016. Links | BibTeX | Tags: Automatic License Plate Recognition, DeepEyes, GigaFrames @inproceedings{Goncalves:2016:ITSC, |
Gabriel Resende Gonçalves; Sirlene Pio Gomes da Silva; David Menotti; William Robson Schwartz Benchmark for License Plate Character Segmentation Journal Article In: Journal of Electronic Imaging, 25 (5), pp. 1-5, 2016, ISBN: 1017-9909. Links | BibTeX | Tags: Automatic License Plate Recognition, Benchmark, Character Segmentation, DeepEyes, Featured Publication, GigaFrames, Jaccard Coefficient, Novel Dataset, Sense SegPlate @article{2016:JEI:Gabriel, |
Gabriel Resende Goncalves License Plate Recognition based on Temporal Redundancy Masters Thesis Federal University of Minas Gerais, 2016. Abstract | Links | BibTeX | Tags: Automatic License Plate Recognition, DeepEyes, GigaFrames @mastersthesis{Goncalves:2016:MSc,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. |
2015 |
Sirlene Peixoto; Gabriel Resende Gonçalves; Guillermo Camara-Chavez; William Robson Schwartz; David Menotti Gomes Brazilian License Plate Character Recognition using Deep Learning Inproceedings In: Workshop em Visao Computacional (WVC), pp. 1-5, 2015. Links | BibTeX | Tags: Automatic License Plate Recognition, Deep Learning @inproceedings{Peixoto:2015:WVC, |
2014 |
Raphael Felipe Carvalho de Prates; G Camara-Chavez; William Robson Schwartz; D M Gomes An Adaptive Vehicle License Plate Detection at Higher Matching Degree Inproceedings In: Iberoamerican Congress on Pattern Recognition (CIARP), pp. 454-461, Springer International Publishing, 2014. Links | BibTeX | Tags: Automatic License Plate Recognition, DET, License Plate Detection @inproceedings{Prates:2014:CIARP, |
2013 |
Raphael Felipe Carvalho de Prates; G Camara-Chavez; William Robson Schwartz; D Menotti Brazilian License Plate Detection Using Histogram of Oriented Gradients and Sliding Windows Journal Article In: International Journal of Computer Science and Information Technology, 5 , pp. 39-52, 2013. Links | BibTeX | Tags: ARDOP, Automatic License Plate Recognition, HOG, License Plate Detection @article{Prates:2013:IJCSIT, |