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http://dx.doi.org/10.5573/ieie.2017.54.2.115

Deep Learning Based Sign Detection and Recognition for the Blind  

Jeon, Taejae (School of Electrical and Electronic Engineering, Yonsei University)
Lee, Sangyoun (School of Electrical and Electronic Engineering, Yonsei University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.54, no.2, 2017 , pp. 115-122 More about this Journal
Abstract
This paper proposes a deep learning algorithm based sign detection and recognition system for the blind. The proposed system is composed of sign detection stage and sign recognition stage. In the sign detection stage, aggregated channel features are extracted and AdaBoost classifier is applied to detect regions of interest of the sign. In the sign recognition stage, convolutional neural network is applied to recognize the regions of interest of the sign. In this paper, the AdaBoost classifier is designed to decrease the number of undetected signs, and deep learning algorithm is used to increase recognition accuracy and which leads to removing false positives which occur in the sign detection stage. Based on our experiments, proposed method efficiently decreases the number of false positives compared with other methods.
Keywords
deep learning; sign detection and recognition; blind assistance system; AdaBoost classifier; convolutional neural network;
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1 C. Yoon, S. Jang and M. Park, "Real-time road sign detection using vertical plane and adaboost," Journal of the Institute of Electronics Engineers of Korea SC, vol. 46, no.5, pp. 29-37, Sep. 2009.
2 J. Kim and J. Park, "Traffic sign detection using the HSI eigen-color model and invariant moments," Journal of the Institute of Electronics Engineers of Korea CI, vol. 47, no.1, pp. 41-51, Jan. 2010.
3 B. Tian, R. Chen, Y. Yao and N. Li, "Robust traffic sign detection in complex road environments," Vehicular Electronics and Safety (ICVES), 2016 IEEE International Conference on. IEEE, pp. 1-5, Jul. 2016.
4 Y. Bengio, A. Courville and P. Vincent, "Representation learning: A review and new perspectives," IEEE transactions on pattern analysis and machine intelligence, vol.35, no.8, pp. 1798-1828, 2013.   DOI
5 Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li and S. Hu, "Traffic-Sign Detection and Classification in the Wild," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2110-2118, 2016.
6 M. Peemen, B. Mesman and H. Corporaal, "Speed sign detection and recognition by convolutional neural networks," 8th International Automotive Congress, pp. 162-170, 2011.
7 P. Dollár, R. Appel, S. Belongie and P. Perona, "Fast feature pyramids for object detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, no.8, pp. 1532-1545, 2014.   DOI
8 P. Viola and M. Jones, "Robust Real Time Object Detection," IEEE ICCV Workshop Statistical and Computational Theories of Vision, Jul. 2001.
9 D. CireşAn, U. Meier, J. Masci and J. Schmidhuber, "Multi-column deep neural network for traffic sign classification," Neural Networks, vol.32, pp. 333-338, 2012.   DOI
10 Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick and T. Darrell, "Caffe: Convolutional architecture for fast feature embedding," Proceedings of the 22nd ACM international conference on Multimedia. ACM, pp. 675-678, Nov. 2014.
11 R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587, 2014.