Rotation Invariant Recognition of Road Signs with Ensemble of 1-NN Neural Classifiers
The paper presents a parallel system of two compound classifiers for recognition of the circular shape road signs. Each of the two classifiers is built of an ensemble of 1-nearest-neighbour (1-NN) classifiers and the arbitration unit operating in the winner-takes-all mode. For the 1-NN we employed the Hamming neural network (HNN) which accepts the binary input. Each HNN is responsible for classification within a single group of deformable prototypes of the road signs. Each of the two compound classifiers has the same structure, however they accept features from different domains: the spatial and the log-polar spaces. The former has an ability of precise classification for shifted but non-rotated objects. The latter exhibits good abilities to register the rotated shapes and also to reject the non road sign objects due to its high false negative detection properties. The combination of the two outperformed each of the single versions what was verified experimentally. The system is characterized by fast learning and recognition rates.
KeywordsInput Pattern Natural Scene Road Sign Recognition Module Binary Input
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- 2.Amit, Y.: 2D Object Detection and Recognition. MIT Press, Cambridge (2002)Google Scholar
- 3.Aoyagi, Y., Asakura, T.: A study on traffic sign recognition in scene image using genetic algorithms and neural networks. In: IEEE Conf. Electronics, Control, pp. 1838–1843 (1996)Google Scholar
- 7.DaimlerChrysler, The Thinking Vehicle (2002), http://www.daimlerchrysler.com
- 8.Duch, W., Grudziński, K.: A framework for similarity-based methods. Second Polish Conference on Theory and Applications of Artificial Intelligence, 33–60 (1998)Google Scholar
- 11.Floréen, P.: Computational Complexity Problems in Neural Associative Memories. PhD Thesis, University of Helsinki, Department of Computer Science, Finland (1992)Google Scholar
- 14.Lippman, R.: An introduction to computing with neural nets. IEEE Transactions on Acoustic, Speech, and Signal Processing ASSP-4, 4–22 (1987)Google Scholar
- 15.Luo, R.C., Potlapalli, H.: Landmark recognition using projection learning for mobile robot navigation. In: Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 2703–2708 (1994)Google Scholar
- 18.Road Signs and Signalization. Directive of the Polish Ministry of Infrastructure, Internal Affairs and Administration (Dz. U. Nr 170, poz. 1393) (2002)Google Scholar
- 19.Zheng, Y.J., Ritter, W., Janssen, R.: An adaptive system for traffic sign recognition. In: Proc. IEEE Intelligent Vehicles Symp., pp. 165–170 (1994)Google Scholar