Automatic large-scale data acquisition via crowdsourcing for crosswalk classification: A deep learning approach
Tài liệu tham khảo
WHO: World Health Organization. Visual impairment and blindness. 2014. URL http://www.who.int/mediacentre/factsheets/fs282/en/.
Ivanchenko, 2008, Detecting and locating crosswalks using a camera phone, 1
Wang, 2014, Rgb-d image-based detection of stairs, pedestrian crosswalks and traffic signs, J Vis Commun Image Represent, 25, 263, 10.1016/j.jvcir.2013.11.005
Riveiro, 2015, Automatic detection of zebra crossings from mobile LiDAR data, Opt Laser Technol, 70, 63, 10.1016/j.optlastec.2015.01.011
Ghilardi, 2016, Crosswalk localization from low resolution satellite images to assist visually impaired people, IEEE Comput Gr Appl, 10.1109/MCG.2016.50
Foucher, 2011, Detection and recognition of urban road markings using images, 1747
Berriel, 2017, Ego-Lane Analysis System (ELAS): Dataset and Algorithms, Image Vis Comput, 10.1016/j.imavis.2017.07.005
Berriel, 2017, Deep learning based large-scale automatic satellite crosswalk classification, IEEE Geosci Remote Sens Lett, 10.1109/LGRS.2017.2719863
Poggi, 2015, Crosswalk recognition through point-cloud processing and deep-learning suited to a wearable mobility aid for the visually impaired, 282
Ahmetovic, 2015, Zebra crossing spotter: automatic population of spatial databases for increased safety of blind travelers, 251
Hara, 2015, Characterizing and visualizing physical world accessibility at scale using crowdsourcing, computer vision, and machine learning, ACM SIGACCESS Access Comput, 13, 10.1145/2850440.2850442
Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. Arxiv:1409.1556.
Russakovsky, 2015, ImageNet large scale visual recognition challenge, Int J Comput Vis (IJCV), 115, 211, 10.1007/s11263-015-0816-y
Glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, 9, 249
Casella, 2002, 2
Chetlur S., Woolley C., Vandermersch P., Cohen J., Tran J., Catanzaro B., et al. cuDNN: efficient primitives for deep learning. 2014. Arxiv:1410.0759.
Jia Y., Shelhamer E., Donahue J., Karayev S., Long J., Girshick R., et al. Caffe: convolutional architecture for fast feature embedding. 2014. Arxiv:1408.5093.
Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, 1097
de Paula, 2014, Automatic on-the-fly extrinsic camera calibration of onboard vehicular cameras, Expert Syst Appl, 41, 1997, 10.1016/j.eswa.2013.08.096