Abstract
Visually impaired persons often face difficulty to move around in new environment, especially on road. Information about the presence of different objects in his/her surroundings can help the person to move independently. This work focuses on development of a Convolution Neural Network (CNN) based portable assistive system for visually impaired people. The system will provide real-time auditory description of the object appearing in the visually challenged person’s way along with its relative location. This assists the person to move independently inside house and workplace or on streets. The proposed system will capture real-time video through the camera of the android mobile set of the user (visually challenged person). The video input will be processed by trained CNN model and the object classification result will be used to compute the relative location of the object and to generate the warning for collision in form of auditory description for the user.
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Maheshwari, C., Kumar, P., Gupta, A., Bandyopadhyay, O. (2022). VISION HELPER: CNN Based Real Time Navigator for the Visually Impaired. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_20
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DOI: https://doi.org/10.1007/978-3-031-11346-8_20
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