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dc.contributor.authorImesha, KLH
dc.contributor.authorGayamini, G
dc.contributor.authorHettige, B
dc.date.accessioned2022-09-01T06:06:19Z
dc.date.available2022-09-01T06:06:19Z
dc.date.issued2022-08
dc.identifier.urihttp://ir.kdu.ac.lk/handle/345/5769
dc.description.abstractTravelling is one of the biggest problems faced by the visually impaired community in their day today life. Even though the visually impaired people have their own methods for travelling such as with the aid of white canes and guide dogs, those traditional methods have lot of difficulties and i ssues. Because of those issues, researches has paid attention to develop assistive technologies which can help the visually impaired community for their day today travelling. Although the assistant technologies are developed in the past decades, the visually impaired community faces a lot of issues when using them because of the Disability Digital Devide. As a result of this, number of systems and technologies have been introduced to the word. Those systems use different technologies like vision modules, sensors and GIS maps but most of those systems are visionbased and they use different approaches to develop their vision modules such as deep learning, reinforcement learning, stereo imaging, enhanced image processing techniques, etc. So in this paper, we will focus on those vision-based systems to find the optimal way of developing a vison-based assistant system for visually impaired people by studying those technologies and by comparing and contrasting those technologies while understanding their used modules algorithms, efficiency, usability, functionalities and their advantages and disadvantages.en_US
dc.language.isoenen_US
dc.subjectComputer Visionen_US
dc.subjectDeep Learningen_US
dc.subjectSmart Wearableen_US
dc.titleA Review on Vision-Based Obstacle Avoidance and Assistant Systems for Visually Impaired Peopleen_US
dc.typeArticle Full Texten_US
dc.identifier.journalInternational Journal of Research in Computing -2022en_US
dc.identifier.issue2en_US
dc.identifier.volume1en_US
dc.identifier.pgnos15-22en_US


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