Pregled bibliografske jedinice broj: 808608
Multiple-dataset Traffic Sign Classification with OneCNN
Multiple-dataset Traffic Sign Classification with OneCNN // Third IAPR Asian Conference on Pattern Recognition / Kise, Koichi ; Wang, Liang ; Remagnino, Paolo ; Byun, Hyeran (ur.).
Kuala Lumpur, Malezija, 2015. str. 1-5 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Multiple-dataset Traffic Sign Classification with OneCNN
Autori
Jurišić, Fran ; Filković, Ivan ; Kalafatić, Zoran
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Third IAPR Asian Conference on Pattern Recognition
/ Kise, Koichi ; Wang, Liang ; Remagnino, Paolo ; Byun, Hyeran - , 2015, 1-5
Skup
IAPR Asian Conference on Pattern Recognition
Mjesto i datum
Kuala Lumpur, Malezija, 03.11.2015. - 06.11.2015
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
machine learning ; deep learning ; convolutional neural networks ; classification
Sažetak
We take a look at current state of traffic sign classification discussing what makes it a specific problem of visual object classification. With impressive state-of-the- art results it is easy to forget that the domain extends beyond annotated datasets and overlook the problems that must be faced before we can start training classifiers. We discuss such problems, give an overview of previous work done, go over publicly available datasets and present a new one. Following that, classification experiments are conducted using a single CNN model, deeper than used previously and trained with dropout. We apply it over multiple datasets from Germany, Belgium and Croatia, their intersections and union, outperforming humans and other single CNN architectures for traffic sign classification.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb