Open Access
Issue |
ITM Web Conf.
Volume 37, 2021
International Conference on Innovative Technology for Sustainable Development (ICITSD-2021)
|
|
---|---|---|
Article Number | 01019 | |
Number of page(s) | 10 | |
Section | Innovative Technology for Sustainable Development | |
DOI | https://doi.org/10.1051/itmconf/20213701019 | |
Published online | 17 March 2021 |
- S. Tosun and E. Karaarslan, “Real-Time Object Detection Application for Visually Impaired People: Third Eye”, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey [Google Scholar]
- N. M. Tembhurne, S. V. Vaidya, A. Shiekh, S. Dravyakar, “Voice Assistant for Visually Impaired People”, International Research Journal of Engineering and Technology (IRJET). [Google Scholar]
- D. Dahiya, H. Gupta and M. K. Dutta, “A Deep Learning based Real-Time Assistive Framework for Visually Impaired”, 2020 International Conference on Contemporary Computing and Applications (IC3A), Lucknow, India, 2020 [Google Scholar]
- F. Ahmed, M. S. Mahmud, and M. Yeasin, “RNN and CNN for Way-Finding and Obstacle Avoidance for Visually Impaired”, 2019 2nd International Conference on Data Intelligence and Security (ICDIS), South Padre Island, TX, USA, 2019 [Google Scholar]
- S. Gianani, A. Mehta, T. Motwani, and R. Shende, “JUVO An Aid for the Visually Impaired”, 2018 International Conference on Smart City and Emerging Technology (ICSCET), Mumbai [Google Scholar]
- R. Kukade, R. Fengse, K. Rodge, S. Ransing, V. Lomte, “Virtual Personal Assistant for the Blind”, 2018 International Journal of Computer Science and Technology (IJCST), Bali, Indonesia. [Google Scholar]
- M. A. Khan Shishir, S. Rashid Fahim, F. M. Habib, and T. Farah, “Eye Assistant: Using a mobile application to help the visually impaired”, 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh [Google Scholar]
- A. Karthik, V.K. Raja and S. Prabakaran, “Voice Assistance for Visually Impaired People”, 2018 International Conference on Communication, Computing and Internet of Things, Chennai, India [Google Scholar]
- G. Singh, K. Takhtani, O. Kandale, N. Dadhwal, ” A Smart Personal AI Assistant for Visually Impaired People”, Vol 7, Issue 6, pg.1450-54, International Research Journal of Engineering and Technology (IRJET) [Google Scholar]
- V. Sharma, V. M. Singh, S. Thanneeru, ” Virtual Assistant for Visually Impaired”, (April 19, 2020). Available at SSRN:https://ssrn.com/abstract=3580035 [Google Scholar]
- S. A. Jakhete, P. Bagmar, A. Dorle, A. Rajurkar and P. Pimplikar, “Object Recognition App for Visually Impaired”, 2019 IEEE Pune Section International Conference (PuneCon), Pune, India, 2019 [Google Scholar]
- Supersense: https://play.google.com/store/apps/details?id=com.mediate.supersense&hl=en_IN&gl=US [Google Scholar]
- Sullivan+: https://play.google.com/store/apps/details?id=tuat.kr.suliva n&hl=en_IN&gl=US [Google Scholar]
- EnvisionAI: https://play.google.com/store/apps/details?id=com.letsenvision.envisionai&hl=en_IN&gl=US [Google Scholar]
- LetSeeApp: https://play.google.com/store/apps/details?id=com.letseeapp.letseeapp&hl=en_IN&gl=US [Google Scholar]
- “You Only Look Once: Unified, Real-Time Object Detection”: [1506.02640] You Only Look Once: Unified, Real-Time Object Detection (arxiv.org) [Google Scholar]
- SSD: Single Shot MultiBox Detector”: [1512.02325] SSD: Single Shot MultiBox Detector (arxiv.org) [Google Scholar]
- “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”:[1506.01497] Faster RCNN: Towards Real-Time Object Detection with Region Proposal Networks (arxiv.org) [Google Scholar]
- “COCO Common Objects in Context”:COCO Common Objects in Context (cocodataset.org) [Google Scholar]
- ” Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks” [1604.02878] Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (arxiv.org) [Google Scholar]
- ”FaceNet: A Unified Embedding for Face Recognition and Clustering”: [1503.03832] FaceNet: A Unified Embedding for Face Recognition and Clustering (arxiv.org) [Google Scholar]
- GitHub opencv/opencv: Open Source Computer Vision Library [Google Scholar]
- GitHub ageitgey/face_recognition: The world’s simplest facial recognition API for Python and the command line [Google Scholar]
- ” MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices”: [1804.07573] MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices (arxiv.org) [Google Scholar]
- LFW Face Dataset: http://vis-www.cs.umass.edu/lfw [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.