A Smart Device – Helps the Blind to Cross the Road Safely
Priya Sridharan1, Ritwika Ghosh2
1Priya Sridharan, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Ritwika Ghosh, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 July 2019 | Revised Manuscript received on 25 July 2019 | Manuscript Published on 01 August 2019 | PP: 28-30 | Volume-8 Issue-4S2, April 2019 | Retrieval Number: D10080484S219/19©BEIESP | DOI: 10.35940/ijeat.D1008.0484S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Internet of Things has been a flourishing technology used in almost every aspect of modern lives to solve real-world problems. Computer vision, a state of the art technology which helps computers understand digital images, when combined with IoT can augment the existing solutions to make truly intelligent systems. Visually impaired people can use their power of hearing or guide dog to help them cross the road, but they are not much efficient and are prone to accidents as sounds subside and they cannot pace with the speed of a dog. Existing technologies which help blind people navigate do not have a viable mechanism for taking accidents and safety into account. Visually impaired people have enhanced touch abilities. Leveraging this skill, this paper aims at providing a solution that produces haptic feedback in the form of vibrations indicating the suitable time to cross the road. A camera provides live data of their surroundings, which detects the traffic signal and determines its colour with the help of computer vision. If the signal is red, an indication in the form of haptic feedback is produced using a vibrating mini disc motor, thus helping the visually impaired person to cross the road safely, thereby preventing accidents.
Keywords: Deep Learning Image Processing, IoT, Object Detection, Smart Environments.
Scope of the Article: Deep Learning