Abstract
This paper presents a wearable navigation assistive system for the blind and the visually impaired built with off-the-shelf technology. Microsoft Kinect’s on board depth sensor is used to extract Red, Green, Blue and Depth (RGB-D) data of the indoor environment. Speeded-Up Robust Features (SURF) and Bag-of-Visual-Words (BOVW) model is used to extract features and reduce generic indoor object detection into a machine learning problem. A Support Vector Machine classifier is used to classify scene objects and obstacles to issue critical real-time information to the user through an external aid (earphone) for safe navigation. We performed a user-study with blind-fold users to measure the efficiency of the overall framework.
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Bhowmick, A., Prakash, S., Bhagat, R., Prasad, V., Hazarika, S.M. (2014). IntelliNavi: Navigation for Blind Based on Kinect and Machine Learning. In: Murty, M.N., He, X., Chillarige, R.R., Weng, P. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2014. Lecture Notes in Computer Science(), vol 8875. Springer, Cham. https://doi.org/10.1007/978-3-319-13365-2_16
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DOI: https://doi.org/10.1007/978-3-319-13365-2_16
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