Browse > Article
http://dx.doi.org/10.17661/jkiiect.2016.9.3.249

Deep learning based symbol recognition for the visually impaired  

Park, Sangheon (Electronics and Telecommunications Research Institute)
Jeon, Taejae (School of Electrical and Electronic Engineering, Yonsei University)
Kim, Sanghyuk (School of Electrical and Electronic Engineering, Yonsei University)
Lee, Sangyoun (School of Electrical and Electronic Engineering, Yonsei University)
Kim, Juwan (Electronics and Telecommunications Research Institute)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.9, no.3, 2016 , pp. 249-256 More about this Journal
Abstract
Recently, a number of techniques to ensure the free walking for the visually impaired and transportation vulnerable have been studied. As a device for free walking, there are such as a smart cane and smart glasses to use the computer vision, ultrasonic sensor, acceleration sensor technology. In a typical technique, such as techniques for finds object and detect obstacles and walking area and recognizes the symbol information for notice environment information. In this paper, we studied recognization algorithm of the selected symbols that are required to visually impaired, with the deep learning algorithm. As a results, Use CNN(Convolutional Nueral Network) technique used in the field of deep-learning image processing, and analyzed by comparing through experimentation with various deep learning architectures.
Keywords
Convolutional neural network; Deep learning; Deep neural network; Machine learning; Symbol recognization;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 J. Choi, G. Jeong, "Development of Walking Assist Smartphone Case for Blind People", The Journal of Korea Institute of Information, Electronics, and Communication Technology, Vol.8, No.3, pp.239-242, 2015.   DOI
2 C. Kang, H. Jo, B. Kim, "A Machine-to-machine based Intelligent Walking Assistance System for Visually Impaired Person", The Journal of The Korean Institute of Communication Sciences, Vol.36, No.3, pp.287-296, 2011.
3 P. Sermanet, K. Kavukcuoglu, S. Chintala and Y. LeCun, "Pedestrian detection with unsupervised multi-stage feature learning", Proc. IEEE Conference on Computer Vision Pattern Recognition (CVPR), pp.3626-3633, 2013.
4 D. Dajun and C. Lee, "Fast algorithm for Traffic Sign Recognition", Journal of IKEEE, Vol.16, No.4, pp.356-363, December 2012.   DOI
5 W. W. Zhu, et al, "Searching for Pulsars Using Image Pattern Recognition", The Astrophysical Journal, Vol.781, No.2, pp.117-128, 2014.   DOI
6 A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, pages 1106-1114, 2012.
7 N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting", The Journal of Machine Learning Research, Vol.15, Issue.1, pp.1929-1958, 2014.
8 Y. Jia, et al. "Caffe: Convolutional architecture for fast feature embedding", Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675-678, 2014.