Abstract
Traffic sign recognition is one of the hot issues on the modern driving assistance. In recent years, the method using Bag-of-Word (BOW) model for image recognition has gained its popularity upon its simplicity and efficiency. The conventional approach based on BOW requires nonlinear classifiers to get a good image recognition accuracy. Instead, a method called Locality-constrained Linear Coding(LLC) presents an effective strategy for coding, and only with a simple linear classifier could achieve a good effect. LLC uses uniform sampling for feature extraction, but allowing for features of traffic signs, the central vision information of the image is more important than the surroundings. Fortunately, log-polar mapping to preprocess image samples before coding is helpful for traffic sign recognition. In this paper, a combination method of log-polar mapping and LLC algorithm is presented to achieve a high image classification performance up to 97.3141% on speed limit sign in the GTSRB dataset.
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References
Liu, W., Lv, J., Gao, H., Duan, B., Yuan, H., Zhao, H.: An efficient real-time speed limit signs recognition based on rotation invariant feature. In: Proc. of Intelligent Vehicles Symposium (IV), pp. 1000–1005 (2011)
Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: Proc. of Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 524–531 (2005)
Kardkovacs, Z., Paroczi, Z., Varga, E., Siegler, A., Lucz, P.: Real-time traffic sign recognition system. In: Proc. of Second International Conference on Cognitive Infocommunications (CogInfoCom), pp. 1–5 (2011)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proc. of Computer Vision and Pattern Recognition (CVPR), pp. 2169–2178 (2006)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. of Comp. Vision 60, 91–110 (2004)
Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gomez-Moreno, H., Lopez-Ferreras, F.: Road-sign detection and recognition based on support vector machines. IEEE Trans. on Intelligent Transportation Systems 8, 264–278 (2007)
Paclik, P.: Road sign recognition survey, http://euler.fd.cvut.cz/research/rs2/files/skoda-rs-survey.html
Ruta, A., Li, Y., Liu, X.: Robust class similarity measure for traffic sign recognition. IEEE Trans. on Intelligent Transportation Systems 11, 846–855 (2010)
Traver, V., Bernardino, A.: A review of log-polar imaging for visual perception in robotics. Robotics and Autonomous Systems 58, 378–398 (2010)
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: Proc. of Computer Vision and Pattern Recognition (CVPR), pp. 3360–3367 (2010)
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: Proc. of Computer Vision and Pattern Recognition (CVPR), pp. 1794–1801 (2009)
Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: Proc. of Advances in Neural Information Processing Systems (NIPS), pp. 1–9 (2009)
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Liu, B., Liu, H., Luo, X., Sun, F. (2012). Speed Limit Sign Recognition Using Log-Polar Mapping and Visual Codebook. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_28
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DOI: https://doi.org/10.1007/978-3-642-31362-2_28
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