Name
Abstract | ||
|---|---|---|
In this paper, a system to aid the visually impaired by providing contextual information of the surroundings using 360° view camera combined with deep learning is proposed. The system uses a 360° view camera with a mobile device to capture surrounding scene information and provide contextual information to the user in the form of audio. The scene information from the spherical camera feed is classified by identifying objects that contain contextual information of the scene. That is achieved using convolutional neural networks (CNN) for classification by leveraging CNN transfer learning properties using the pre-trained VGG-19 network. There are two challenges related to this paper, a classification and a segmentation challenge. As an initial prototype, we have experimented with general classes such restaurants, coffee shops and street signs. We have achieved a 92.8% classification accuracy in this paper. |
Year | Venue | Field |
|---|---|---|
2017 | SMC | Computer vision,Computer science,Segmentation,Visualization,Convolutional neural network,Transfer of learning,Support vector machine,Feature extraction,Mobile device,Artificial intelligence,Deep learning,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mazin Ali | 1 | 0 | 0.34 |
| Ferat Sahin | 2 | 706 | 45.49 |
| Shitij Kumar | 3 | 1 | 1.73 |
| Celal Savur | 4 | 0 | 0.34 |