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360° view camera based visual assistive technology for contextual scene information

机译:360°查看基于相机的上下文场景信息的视觉辅助技术

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摘要

In this research project, a system is proposed to aid the visually impaired by providing partial 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 system could also be used for other applications such as logo detection which visually impaired users can use for shopping assistance.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 research project.
机译:在该研究项目中,提出了一种系统,以帮助使用360°视图相机与深度学习相结合的周围环境的部分上下文信息来帮助视力损害。该系统使用具有移动设备的360°视图相机来捕获周围的场景信息,并以音频的形式向用户提供上下文信息。该系统也可以用于其他应用程序,例如可视障碍用户可以用于购物帮助的徽标检测。来自球面相机馈送的场景信息通过识别包含场景的上下文信息的对象来分类。这是使用卷积神经网络(CNN)来通过利用CNN传输学习属性使用预先训练的VGG-19网络来实现。与本文有两种挑战,分类和分割挑战。作为初始原型,我们已经尝试了一般的课堂,咖啡店和街道标志。我们在本研究项目中取得了92.8%的分类准确性。

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