Digital Enhancement of Cultural Experience and Accessibility for the Visually Impaired

Iakovidis, Dimitris K.; Diamantis, Dimitrios E.; Dimas, George; Ntakolia, Charis; Spyrou, Evaggelos

doi:10.1007/978-3-030-16450-8_10

Cited by 15 publications

(12 citation statements)

References 80 publications

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“…Image-based obstacle detection is a component of major importance for assistive navigation systems for the VCP. A user requirement analysis [17], revealed that the users need a system that aims to real-time performance and mainly detects vertical objects, e.g., trees, humans, stairs, and ground anomalies.…”

Section: Obstacle Detectionmentioning

confidence: 99%

“…This enables CNNs to automatically extract features from the entire image, instead of relying on hand-crafted features, such as color and texture. Multiple CNN architectures have been proposed over the last years, each one contributing some unique characteristics [17]. Although conventional CNN architectures, such as the Visual Geometry Group Network (VGGNet) [40], offer great classification performance, they usually require large, high-end workstations equipped with Graphical Processing Units (GPUs) to execute them.…”

Section: Object Recognitionmentioning

confidence: 99%

“…The wearable device, in the form of smart glasses, was designed using a CAD software according to the user requirements listed in [17]. The most relevant to the design requirements mentioned that the wearable system should be attractive and elegant, possibly with a selection of different colors, but in a minimalist rather than attention grabbing way.…”

Section: Smart Glasses Designmentioning

confidence: 99%

“…The most relevant to the design requirements mentioned that the wearable system should be attractive and elegant, possibly with a selection of different colors, but in a minimalist rather than attention grabbing way. In terms of construction, the system should be robust; last a long time, not requiring maintenance; and be resistant to damage, pressure, knocks and bumps, water, and harsh weather conditions [17].…”

Section: Smart Glasses Designmentioning

confidence: 99%

“…In the rest 10,277 video frames, the person was walking until collision, towards obstacles considered as threatening, which should be detected and recognized. The intervals determining whether an obstacle is considered as threatening or not were set according to the user requirements established by VCP for obstacle detection tasks in [17]. Regarding that, the desired detection distance for the early avoidance of an obstacle according to the VCP user requirements is up to 2 m.…”

Section: Experimental Frameworkmentioning

confidence: 99%

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Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged

Dimas

Diamantis

Kalozoumis

et al. 2020

Sensors

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Every day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a novel visual perception system for outdoor navigation that can be evolved into an everyday visual aid for VCP. The proposed methodology is integrated in a wearable visual perception system (VPS). The proposed approach efficiently incorporates deep learning, object recognition models, along with an obstacle detection methodology based on human eye fixation prediction using Generative Adversarial Networks. An uncertainty-aware modeling of the obstacle risk assessment and spatial localization has been employed, following a fuzzy logic approach, for robust obstacle detection. The above combination can translate the position and the type of detected obstacles into descriptive linguistic expressions, allowing the users to easily understand their location in the environment and avoid them. The performance and capabilities of the proposed method are investigated in the context of safe navigation of VCP in outdoor environments of cultural interest through obstacle recognition and detection. Additionally, a comparison between the proposed system and relevant state-of-the-art systems for the safe navigation of VCP, focused on design and user-requirements satisfaction, is performed.

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Section: Obstacle Detectionmentioning

confidence: 99%

Section: Object Recognitionmentioning

confidence: 99%

Section: Smart Glasses Designmentioning

confidence: 99%

Section: Smart Glasses Designmentioning

confidence: 99%

Section: Experimental Frameworkmentioning

confidence: 99%

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