Machine Learning Based Computer Vision Application for Visually Disabled People

Author:

Mone Shubhada1,Salunke Nihar2,Jadhav Omkar,Barge Arjun,Magar Nikhil

Affiliation:

1. Faculty at Department of Computer Engineering, SPPU, India

2. Department of Computer Engineering, SPPU, India

Abstract

With the easy availability of technology, smartphones are playing an important role in every person’s life. Also, with the advancements in computer vision based research, Automatic Driving cars, Object Recognition, Depth Map Prediction, Object Distance Estimation, have reached commendable levels of intelligence and accuracy. Combining the research and technological advancements, we can be hopeful in creating a computer vision based mobile-application which will help guide visually disabled people in performing their day to day tasks with easily available mobile applications. With our study, the visually disabled can perform simple tasks like outdoor/indoor navigation without encountering obstacles, also they can avoid accidental collisions with objects in their surroundings. Currently, there are very few applications which provide the same assistance to the visually impaired. Using physical tools like sticks is a very common practice when it comes to avoiding obstacles in a visually disabled person’s path. Our study will be focused on object detection and depth estimation techniques- two of the most popular and advanced fields in Intelligent Computer vision studies. We have explored more on the traditional challenges and future hopes of incorporating these techniques on embedded devices.

Publisher

Technoscience Academy

Subject

General Medicine

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. PEDESTRIAN DETECTION FOR VISUALLY DISABLED USING COMPUTER VISION;2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS);2024-04-18

2. Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications;Baghdad Science Journal;2023-12-05

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