Features extraction for traffic sign recognition

Research Article
Open access

Features extraction for traffic sign recognition

Tianyi Wang 1 , Jianlin Dou 2* , Junyi Wu 3 , Hang Zhang 4 , Yunhua Lu 5
  • 1 Pennsylvania State University    
  • 2 The Ohio State University-Columbus    
  • 3 Imperial College London    
  • 4 New York University    
  • 5 Guangdong Experience High School    
  • *corresponding author pochooge@gmail.com
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230891
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Traffic sign recognition (TSR) is the basic technology of the Advanced Driving Assistance System(ADAS) and intelligent automobile, while a highly qualified feature vector plays a key role in TSR. Therefore, the feature extraction of TSR has become active research in the fields of computer vision and intelligent automobiles. Although deep learning features have made a breakthrough in image classification, it is difficult to apply to TSR because of its large scale of training dataset and high space-time complexity of model training. Considering visual characteristics of traffic signs and external factors such as weather, light, and blur in real scenes, an efficient method to extract high-qualified image features is proposed. As a result, the lower-dimension feature can accurately depict the visual feature of TSR due to its powerful descriptive and discriminative ability. In addition, benefiting from a simple feature extraction method and lower time cost, our method is suitable to recognize traffic signs online in real-world applications scenarios. Extensive quantitative experimental results demonstrate the effectiveness and efficiency of our method.

Keywords:

Traffic Sign Recognition, Advanced Driving Assistance System, Neural Network, Feature Extraction

Wang,T.;Dou,J.;Wu,J.;Zhang,H.;Lu,Y. (2023). Features extraction for traffic sign recognition. Applied and Computational Engineering,6,1682-1692.
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References

[1]. C. J. C. S. Ardianto and H. M. Hang, “Real-time traffic sign recognition using color segmentation and svm,” in 2017 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1–5, 2017.

[2]. W. Li, H. Song, and P. Wang, “Finely crafted features for traffic sign recognition,” International Journal of Circuits, Systems and Signal Processing, vol. 16, pp. 159–170, 2022.

[3]. I. S. A. Krizhevsky and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25, Vol.25, pp. 1097–1150, 2012.

[4]. J. S. J. Stallkamp, M. Schlipsing and C. Igel, “The german traffic sign recognition benchmark: A multi-class classification competition,” in International Joint Conference on Neural Networks, pp. 1453–1460, 2011.

[5]. F. Qin, B. Fang, and H. Zhao, “Traffic sign segmentation and recognition in scene images,” in 2010 Chinese Conference on Pattern Recognition (CCPR), pp. 1–5, IEEE, 2010.

[6]. Y.-Y. Nguwi and A. Z. Kouzani, “Automatic road sign recognition using neural networks,” in The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 3955–3962, IEEE, 2006.

[7]. N. B. Romdhane, H. Mliki, and M. Hammami, “An improved traffic signs recognition and tracking method for driver assistance system,” in 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pp. 1–6, IEEE, 2016.

[8]. Y. R. Fatmehsari, A. Ghahari, and R. A. Zoroofi, “Gabor wavelet for road sign detection and recognition using a hybrid classifier,” in 2010 International Conference on Multimedia Computing and Information Technology (MCIT), pp. 25–28, IEEE, 2010.

[9]. Z. Zheng, H. Zhang, B. Wang, and Z. Gao, “Robust traffic sign recognition and tracking for advanced driver assistance systems,” in 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 704– 709, IEEE, 2012.

[10]. M. A. Garcia-Garrido, M. A. Sotelo, and E. Martin-Gorostiza, “Fast traffic sign detection and recognition under changing lighting conditions,” in 2006 IEEE Intelligent Transportation Systems Conference, pp. 811–816, IEEE, 2006.

[11]. N. Barnes and A. Zelinsky, “Real-time radial symmetry for speed sign detection,” in IEEE Intelligent Vehicles Symposium, 2004, pp. 566–571, IEEE, 2004.

[12]. Z. Zheng, H. Zhang, B. Wang, and Z. Gao, “Robust traffic sign recognition and tracking for advanced driver assistance systems,” in 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 704– 709, IEEE, 2012.

[13]. H. Gómez-Moreno, S. Maldonado-Bascón, P. Gil-Jiménez, and S. Lafuente- Arroyo, “Goal evaluation of segmentation algorithms for traffic sign recognition,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 4, pp. 917–930, 2010.

[14]. Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu, “Traffic-sign detection and classification in the wild,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2110–2118, 2016.

[15]. S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems, vol. 28, 2015.

[16]. H. Supreeth and C. M. Patil, “An approach towards efficient detection and recognition of traffic signs in videos using neural networks,” in 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 456–459, IEEE, 2016.

[17]. M. M. Lau, K. H. Lim, and A. A. Gopalai, “Malaysia traffic sign recognition with convolutional neural network,” in 2015 IEEE international conference on digital signal processing (DSP), pp. 1006–1010, IEEE, 2015.

[18]. C. Liu, F. Chang, and Z. Chen, “High performance traffic sign recognition based on sparse representation and svm classification,” in 2014 10th International Conference on Natural Computation (ICNC), pp. 108–112, IEEE, 2014.

[19]. S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” in 2017 international conference on engineering and technology (ICET), pp. 1–6, Ieee, 2017.

[20]. M. Xing, M. Chunyang, W. Yan, W. Xiaolong, and C. Xuetao, “Traffic sign detection and recognition using color standardization and zernike moments,” in 2016 Chinese Control and Decision Conference (CCDC), pp. 5195–5198, IEEE, 2016.

[21]. P. Carcagnì, M. Del Coco, M. Leo, and C. Distante, “Facial expression recognition and histograms of oriented gradients: a comprehensive study,” SpringerPlus, vol. 4, p. 645, 11 2015.

[22]. S.-H. Lee, M. Bang, K.-H. Jung, and K. Yi, “An efficient selection of hog feature for svm classification of vehicle,” in 2015 International Symposium on Consumer Electronics (ISCE), pp. 1–2, 2015.

[23]. W. J.-F. Y. H.-Y. Wang, Xiang-Yang, Robust image retrieval based on color histogram of local feature regions.

[24]. K. Zhang, Z. Fang, J. Liu, and M. Tan, “An adaptive way to detect the racket of the table tennis robot based on hsv and rgb,” vol. 2015, pp. 5936– 5940, 09 2015.

[25]. J. Gordon, I. Abramov, and H. Chan, “Describing color appearance: Hue and saturation scaling,” Perception psychophysics, vol. 56, pp. 27–41, 08 1994.

[26]. I. Jolliffe and J. Cadima, “Principal component analysis: A review and recent developments,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, p. 20150202, 04 2016.

[27]. Q. Wang, “Kernel principal component analysis and its applications in face recognition and active shape models,” 2012.

[28]. A. Elen, C. Közkurt, and S. Baş, “An adaptive gaussian kernel for support vector machine,” ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, vol. 47, pp. 10579–10588, 03 2022.

[29]. Y. Goldberg and M. Elhadad, “splitSVM: Fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications,” in Proceedings of ACL-08: HLT, Short Papers, (Columbus, Ohio), pp. 237–240, Association for Computational Linguistics, June 2008.

[30]. H. Abdi and L. J. Williams, “Principal component analysis,” Wiley interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp. 433–459, 2010.

Cite this article

Wang,T.;Dou,J.;Wu,J.;Zhang,H.;Lu,Y. (2023). Features extraction for traffic sign recognition. Applied and Computational Engineering,6,1682-1692.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. C. J. C. S. Ardianto and H. M. Hang, “Real-time traffic sign recognition using color segmentation and svm,” in 2017 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1–5, 2017.

[2]. W. Li, H. Song, and P. Wang, “Finely crafted features for traffic sign recognition,” International Journal of Circuits, Systems and Signal Processing, vol. 16, pp. 159–170, 2022.

[3]. I. S. A. Krizhevsky and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25, Vol.25, pp. 1097–1150, 2012.

[4]. J. S. J. Stallkamp, M. Schlipsing and C. Igel, “The german traffic sign recognition benchmark: A multi-class classification competition,” in International Joint Conference on Neural Networks, pp. 1453–1460, 2011.

[5]. F. Qin, B. Fang, and H. Zhao, “Traffic sign segmentation and recognition in scene images,” in 2010 Chinese Conference on Pattern Recognition (CCPR), pp. 1–5, IEEE, 2010.

[6]. Y.-Y. Nguwi and A. Z. Kouzani, “Automatic road sign recognition using neural networks,” in The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 3955–3962, IEEE, 2006.

[7]. N. B. Romdhane, H. Mliki, and M. Hammami, “An improved traffic signs recognition and tracking method for driver assistance system,” in 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pp. 1–6, IEEE, 2016.

[8]. Y. R. Fatmehsari, A. Ghahari, and R. A. Zoroofi, “Gabor wavelet for road sign detection and recognition using a hybrid classifier,” in 2010 International Conference on Multimedia Computing and Information Technology (MCIT), pp. 25–28, IEEE, 2010.

[9]. Z. Zheng, H. Zhang, B. Wang, and Z. Gao, “Robust traffic sign recognition and tracking for advanced driver assistance systems,” in 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 704– 709, IEEE, 2012.

[10]. M. A. Garcia-Garrido, M. A. Sotelo, and E. Martin-Gorostiza, “Fast traffic sign detection and recognition under changing lighting conditions,” in 2006 IEEE Intelligent Transportation Systems Conference, pp. 811–816, IEEE, 2006.

[11]. N. Barnes and A. Zelinsky, “Real-time radial symmetry for speed sign detection,” in IEEE Intelligent Vehicles Symposium, 2004, pp. 566–571, IEEE, 2004.

[12]. Z. Zheng, H. Zhang, B. Wang, and Z. Gao, “Robust traffic sign recognition and tracking for advanced driver assistance systems,” in 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 704– 709, IEEE, 2012.

[13]. H. Gómez-Moreno, S. Maldonado-Bascón, P. Gil-Jiménez, and S. Lafuente- Arroyo, “Goal evaluation of segmentation algorithms for traffic sign recognition,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 4, pp. 917–930, 2010.

[14]. Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu, “Traffic-sign detection and classification in the wild,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2110–2118, 2016.

[15]. S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems, vol. 28, 2015.

[16]. H. Supreeth and C. M. Patil, “An approach towards efficient detection and recognition of traffic signs in videos using neural networks,” in 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 456–459, IEEE, 2016.

[17]. M. M. Lau, K. H. Lim, and A. A. Gopalai, “Malaysia traffic sign recognition with convolutional neural network,” in 2015 IEEE international conference on digital signal processing (DSP), pp. 1006–1010, IEEE, 2015.

[18]. C. Liu, F. Chang, and Z. Chen, “High performance traffic sign recognition based on sparse representation and svm classification,” in 2014 10th International Conference on Natural Computation (ICNC), pp. 108–112, IEEE, 2014.

[19]. S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” in 2017 international conference on engineering and technology (ICET), pp. 1–6, Ieee, 2017.

[20]. M. Xing, M. Chunyang, W. Yan, W. Xiaolong, and C. Xuetao, “Traffic sign detection and recognition using color standardization and zernike moments,” in 2016 Chinese Control and Decision Conference (CCDC), pp. 5195–5198, IEEE, 2016.

[21]. P. Carcagnì, M. Del Coco, M. Leo, and C. Distante, “Facial expression recognition and histograms of oriented gradients: a comprehensive study,” SpringerPlus, vol. 4, p. 645, 11 2015.

[22]. S.-H. Lee, M. Bang, K.-H. Jung, and K. Yi, “An efficient selection of hog feature for svm classification of vehicle,” in 2015 International Symposium on Consumer Electronics (ISCE), pp. 1–2, 2015.

[23]. W. J.-F. Y. H.-Y. Wang, Xiang-Yang, Robust image retrieval based on color histogram of local feature regions.

[24]. K. Zhang, Z. Fang, J. Liu, and M. Tan, “An adaptive way to detect the racket of the table tennis robot based on hsv and rgb,” vol. 2015, pp. 5936– 5940, 09 2015.

[25]. J. Gordon, I. Abramov, and H. Chan, “Describing color appearance: Hue and saturation scaling,” Perception psychophysics, vol. 56, pp. 27–41, 08 1994.

[26]. I. Jolliffe and J. Cadima, “Principal component analysis: A review and recent developments,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, p. 20150202, 04 2016.

[27]. Q. Wang, “Kernel principal component analysis and its applications in face recognition and active shape models,” 2012.

[28]. A. Elen, C. Közkurt, and S. Baş, “An adaptive gaussian kernel for support vector machine,” ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, vol. 47, pp. 10579–10588, 03 2022.

[29]. Y. Goldberg and M. Elhadad, “splitSVM: Fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications,” in Proceedings of ACL-08: HLT, Short Papers, (Columbus, Ohio), pp. 237–240, Association for Computational Linguistics, June 2008.

[30]. H. Abdi and L. J. Williams, “Principal component analysis,” Wiley interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp. 433–459, 2010.

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