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An efficient traffic sign recognition based on graph embedding features

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Abstract

Traffic sign recognition (TSR) is one of the significant modules of an intelligent transportation system. It instantly assists the drivers to efficiently recognize the traffic sign. Recognition of traffic sign is a large-scale feature learning problem with different real-world appearances. The main goal of this paper is to develop an efficient TSR method, which can run on an ordinary personal computer (PC). In the proposed method, GIST descriptors of the traffic sign images are extracted and subjected to graph-based linear discriminant analysis to reduce the dimension. Moreover, it effectively learns the discriminative subspace through the graph structure with increased computational efficiency. An efficient TSR module is built by conducting series of experiments using support vector machine, extreme learning machine, and k-nearest neighbor (k-NN) classifiers on available public datasets. Our approach achieved the highest recognition accuracy of 96.33 and 97.79% using k-NN classifier for German Traffic Sign Recognition Benchmark (GTSRB) and Belgium Traffic Sign Classification Benchmark (BelgiumTSC), respectively. Also it achieved 99.1% accuracy for a subcategory of GTSRB traffic signs and able to predict the class of unknown traffic sign within 0.0019 s on an ordinary PC. Hence, it can be used in real-world driver assistance system.

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Notes

  1. http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset.

  2. http://www.vision.ee.ethz.ch/~timofter/traffic_signs/.

  3. http://benchmark.ini.rub.de/?section=gtsrb&subsection=results.

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Authors and Affiliations

  1. Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal University, Manipal, 576104, India

    Anjan Gudigar, Shreesha Chokkadi & U. Raghavendra

  2. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, SUSS University, Clementi, 599489, Singapore

    U. Rajendra Acharya

  3. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    U. Rajendra Acharya

Authors
  1. Anjan Gudigar
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  2. Shreesha Chokkadi
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  3. U. Raghavendra
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  4. U. Rajendra Acharya
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Correspondence to Anjan Gudigar.

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Appendix

Appendix

See Table 7 and Fig. 7.

Table 7 Finally selected features with its F-value on BelgiumTSC dataset
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Fig. 7
figure 7

Complete description of the proposed TSR module

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Gudigar, A., Chokkadi, S., Raghavendra, U. et al. An efficient traffic sign recognition based on graph embedding features. Neural Comput & Applic 31, 395–407 (2019). https://doi.org/10.1007/s00521-017-3063-z

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