Lalita Jaiswar
ABSTRACT
Visually impaired people face several problems in their daily life. One of the biggest problems is to visiting unfamiliar places and identifying public places like pharmacy store, restrooms, pedestrian signs on roads, etc. Although there are some conventional methods that are available to aid visually impaired people but these are inefficient to use without assistance. The proposed method presents a framework which will help visually impaired people to identify the common public amenities while visiting any unfamiliar places. This method uses deep learning for recognizing some daily used places. For this purpose, VGG16 model is used to extract features from the images and train the sequential model. The model has been tested on varying images of different class that are present in the database. The developed algorithm achieves an accuracy of 95.88%. The obtained result of the developed model shows that it is an efficient method for assisting visually impaired people in real time application.
- World Health Organization [Online] Available: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairmentGoogle Scholar
- World Access for the Blind [Online] Available: https://waftb.net/blindness-challenge-and-achievementGoogle Scholar
- D. Dahiya, A. Issac, M. K. Dutta, K. Říha and P. Kříž, "Computer Vision Technique for Scene Captioning to Provide Assistance to Visually Impaired," 2018 41st International Conference on Telecommunications and Signal Processing (TSP), Athens, 2018, pp. 1--4. doi: 10.1109/TSP.2018.8441428Google Scholar
- M. C. Ghilardi, G. Simões, J. Wehrmann, I. H. Manssour and R. C. Barros, "Real-Time Detection of Pedestrian Traffic Lights for Visually-Impaired People," 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, 2018, pp. 1--8. doi: 10.1109/IJCNN.2018.8489516Google Scholar
- B. Mocanu, R. Tapu and T. Zaharia, "Seeing Without Sight --- An Automatic Cognition System Dedicated to Blind and Visually Impaired People," 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, 2017, pp. 1452--1459. doi: 10.1109/ICCVW.2017.172Google Scholar
- J. K. Park, K. S. Han and S. Y. Yun, "Intensity classification background model based on the tracing scheme for deep learning based CCTV pedestrian detection," 2018 IEEE 9th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), Cape Town, 2018, pp. 224--228. doi: 10.1109/ICMIMT.2018.8340453Google Scholar
- S. Yadav and A. Singh, "An image matching and object recognition system using webcam robot," 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, 2016, pp. 282--286. doi: 10.1109/PDGC.2016.7913160Google Scholar
- S. Hayat, S. Kun, Z. Tengtao, Y. Yu, T. Tu and Y. Du, "A Deep Learning Framework Using Convolutional Neural Network for Multi-Class Object Recognition," 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, 2018, pp. 194--198. doi: 10.1109/ICIVC.2018.8492777Google Scholar
- C. Li, Y. Zhang and Y. Qu, "Object detection based on deep learning of small samples," 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI), Xiamen, 2018, pp. 449--454. doi: 10.1109/ICACI.2018.8377501Google Scholar
- S. Chae, M. Kang, J. Sun, B. Kim and S. Ko, "Collision detection method using image segmentation for the visually impaired," in IEEE Transactions on Consumer Electronics, vol. 63, no. 4, pp. 392--400, November2017. doi: 10.1109/TCE.2017.015101Google Scholar
- T. Sharma, J. H. M. Apoorva, R. Lakshmanan, P. Gogia and M. Kondapaka, "NAVI: Navigation aid for the visually impaired," 2016 International Conference on Computing, Communication and Automation (ICCCA), Noida, 2016, pp. 971--976. doi: 10.1109/CCAA.2016.7813856Google Scholar
- K. Simonyan, A. Ziserman, "Very Deep Convolution Networks for Large-Scale Image Recognition" 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings.2015Google Scholar
- Imagenet Large Scale Recognition Challenge 2014(ILSRC) [Online] Available https://www.image-net.org/challenges/LSVRC/2014/Google Scholar
- Using Google Colab [Online] Available: https://medium.com/@oribarel/getting-the-most-out-of-your-google-colab-2b0585f82403Google Scholar
- NotoSans Font Library [Online] Available: https://noto-website-2.storage.googleapis.com/pkgs/NotoSans-hinted.zipGoogle Scholar
Index Terms
- Transfer Learning based Computer Vision Technology for Assisting Visually Impaired for detection of Common Places
Recommendations
Computer Vision based Assistive Technology for Blind and Visually Impaired People
ICCCNT '16: Proceedings of the 7th International Conference on Computing Communication and Networking TechnologiesThe computer vision based assistive technology for the blind and visually impaired is a developing area. The assistive technology helps the visually impaired by providing them with a greater independence. By enabling them with their day-to-day ...
Computer vision-based object recognition for the visually impaired in an indoors environment: a survey
Though several electronic assistive devices have been developed for the visually impaired in the past few decades, however, relatively few solutions have been devised to aid them in recognizing generic objects in their environment, particularly indoors. ...
Assistive technology-based solutions in learning mathematics for visually-impaired people: exploring issues, challenges and opportunities
AbstractIn the absence of vision, visually impaired and blind people rely upon the tactile sense and hearing to obtain information about their surrounding environment. These senses cannot fully compensate for the absence of vision, so visually impaired ...
Comments