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Towards improving the performance of traffic sign recognition using support vector machine based deep learning model

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Abstract

Nowadays autonomous vehicles are evolving due to the advancements in cutting edge technologies. In order to recognize the traffic signatures with high efficacy, traffic recognition system is required. Sign detection and classification are the two parts of the recognition system. The sign detection algorithm detects the size and coordinates of the sign board in an image and in sign classification, the representation of traffic signal is identified and classified into one of their traffic sign sub-classes. In order to achieve these goals, an extremely fast detection module using support vector machine is proposed to detect the traffic sign into one of the traffic classes such as, prohibitory, danger, mandatory, and non-sign. Further classification is carried out using deep convolutional neural networks to determine the sub-classes of each super-class, such as, prohibitory, danger, and mandatory. Based on publicly available benchmark traffic sign image datasets, we have demonstrated that the proposed approach has significantly improved traffic sign recognition accuracy compared with state-of-the-art systems.

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Data Availability

Data sharing not applicable to this article as no datasets were generated during the current study.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600 127, India

    N. G. Bhuvaneswari Amma

  2. School of Computing, Indian Institute of Information Technology, Una, Himachal Pradesh, 177 209, India

    Vikrant Rajput

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  1. N. G. Bhuvaneswari Amma
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  2. Vikrant Rajput
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Correspondence to N. G. Bhuvaneswari Amma.

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Amma, N.G.B., Rajput, V. Towards improving the performance of traffic sign recognition using support vector machine based deep learning model. Multimed Tools Appl 83, 6579–6600 (2024). https://doi.org/10.1007/s11042-023-15479-7

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  • DOI: https://doi.org/10.1007/s11042-023-15479-7

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