A Systematic Review of Stop Line and Zebra Crossing Detection Techniques

Authors

  • Dipak Agrawal , Amit Nayak, Amit Chaudhari , Pravin Jadav

Keywords:

Intelligent vehicle, Road environment recognition, Computer vision

Abstract

This systematic review critically examines and synthesizes the existing literature on stop line and zebra crossing detection techniques, with a focus on enhancing road safety and supporting the visually impaired. The survey encompasses a range of methodologies, including computer vision, image processing, and deep learning, to address the challenges associated with accurate identification and recognition of these critical road elements. Key topics explored include the utilization of Hough transform, curve modeling, flood fill operations, uniform local binary pattern detection, and convolutional neural networks. The review assesses the strengths, limitations, and comparative performance of each technique, highlighting innovations such as adaptive histogram equalization, morphological operations, and regression approaches. The outcomes of this review contribute to a comprehensive understanding of current advancements and pave the way for future research directions in the domain of intelligent transportation systems and assistive technologies for pedestrians, particularly the visually impaired.

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References

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Published

24.03.2024

How to Cite

Amit Nayak, Amit Chaudhari , Pravin Jadav , D. A. ,. (2024). A Systematic Review of Stop Line and Zebra Crossing Detection Techniques . International Journal of Intelligent Systems and Applications in Engineering, 12(3), 2167–2173. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5685

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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