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Pedestrian Crossing Signal Detection System for the Visually Impaired

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Advances in Cognitive Science and Communications (ICCCE 2023)

Part of the book series: Cognitive Science and Technology ((CSAT))

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Abstract

Navigating in outdoor environments can be a challenge for the visually impaired, especially given the increase of vehicular activity on the streets. It is not wise to rely solely on the people involved in the scenario to help the visually impaired person cross the street safely given the number of accidents that happen due to irresponsible driving. Ideas to tackle these problems have been implemented before, using both machine learning and deep learning techniques. Several papers also employ a variety of sensors like proximity sensors, ultrasonic sensors, etc., in order to get relevant feedback in analog format from the surroundings. Camera is one such sensor that can be used to sense the surroundings in order to get relevant digital input. This paper proposes a computer vision-based technique to use such digital input and process it accordingly in order to help the visually challenged tackle the problem. Simple machine learning solutions like SIFT (for feature extraction) are used. Comparison of different classifiers like SVM, K-means, and decision trees has been done to identify the best classifier for a given requirement. Use of simple and efficient methods can be conducive for deployment in real-time systems. Proposed system has a maximum accuracy of 86%.

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Acknowledgements

We express our sincere gratitude to the visually impaired participants in this study, orientation and mobility (O&M) experts and authorities at The Poona Blind Men’s Association, Pune. The authors thank the La Fondation Dassault Systemes for sponsoring, technical support and Vishwakarma Institute of Technology Pune for providing support to carry out this research work.

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, Pune, 411037, India

    Swati Shilaskar, Shubhankar Kalekar, Advait Kamathe, Neeraja Khire, Shripad Bhatlawande & Jyoti Madake

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  1. Swati Shilaskar
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  2. Shubhankar Kalekar
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Correspondence to Swati Shilaskar .

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Editors and Affiliations

  1. Bioaxis DNA Research Centre Private Ltd., Hyderabad, Telangana, India

    Amit Kumar

  2. Glenwood, NSW, Australia

    Stefan Mozar

  3. Nordakademie University of Applied Sciences, Elmshorn, Schleswig-Holstein, Germany

    Jan Haase

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Shilaskar, S., Kalekar, S., Kamathe, A., Khire, N., Bhatlawande, S., Madake, J. (2023). Pedestrian Crossing Signal Detection System for the Visually Impaired. In: Kumar, A., Mozar, S., Haase, J. (eds) Advances in Cognitive Science and Communications. ICCCE 2023. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-8086-2_42

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  • DOI: https://doi.org/10.1007/978-981-19-8086-2_42

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  • Print ISBN: 978-981-19-8085-5

  • Online ISBN: 978-981-19-8086-2

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