Abstract
The problem of obstacle detection for blind people is addressed in this paper. In day-to-day life, visually impaired people face a lot of problems like navigating around places, finding reading material, and arranging clothes. The white stick works in many ways to help people with poor eyesight. The white cane is moved to the side to side to alert the person of possible obstacles in their path. The proposed model detects obstacle on road and raise audio alert, thus making the user aware prior to hitting the obstacle. The dataset contains two classes, namely fallen trees and vehicles. SIFT feature descriptor is used for feature extraction. Dimensionality is reduced by using principal component analysis. The classification performance is observed on five different models. Among SVM ovo, SVM ovr, decision tree, random forest, KNN classifiers, SVM gave highest accuracy of 94.26%.
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Acknowledgements
We express our sincere gratitude to the visually impaired participants in this study, and 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 and technical support, and Vishwakarma Institute of Technology, Pune, for providing support to carry out this research work.
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Shilaskar, S., Desai, R., Bhatlawande, S., Chaudhari, N., Bugad, S., Madake, J. (2023). Detection of Fallen Trees and Vehicles Plying on the Road for Safety of Visually Impaired People. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_7
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DOI: https://doi.org/10.1007/978-981-99-3656-4_7
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