Obstacle Detection Based on Generative Adversarial Networks and Fuzzy Sets for Computer-Assisted Navigation

Dimas, George; Ntakolia, Charis; Iakovidis, Dimitris K.

doi:10.1007/978-3-030-20257-6_46

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…For further evaluation, the proposed method was compared to that proposed in [38], which, on the same dataset, resulted in an accuracy of 72.6% with a sensitivity and specificity of 91.7% and 38.6%, respectively. The method proposed in [38] included neither the ground plane removal in its pipeline nor the personalization aspect. On the other hand, the proposed approach was greatly benefited from these aspects in the minimization of false alarms.…”

Section: Obstacle Detection Resultsmentioning

confidence: 99%

“…Figure 14. Qualitative example of false ground detection as obstacle resulting from using the methodology presented in [38]. In all images, the obstacles are not in a threatening distance.…”

Section: Obstacle Detection Resultsmentioning

confidence: 99%

“…Furthermore, the method is able to detect and notify the user about partially visible obstacles with the condition that the part of the obstacle is: (a) salient, (b) within a distance that would be considered of high risk and (c) at a height that would be affecting the user. In detail, the overall accuracy of the system based on the proposed method was estimated to be 85.7%, when the methodology proposed in [38] produced an accuracy of 72.6%, based on the dataset described in Section 4.1. Additionally, in contrast to other methodologies such as [2,26,27,31,32], the proposed obstacle detection and recognition system is solely based on visual cues obtained using only an RGB-D sensor, minimizing the computational and energy resources required for the integration, fusion, and synchronization of multiple sensors.…”

Section: Discussionmentioning

confidence: 99%

“…A multi-task deep learning model, which estimates the depth of a scene and extracts the obstacles without the need to compute a global map with an application in micro air vehicle flights, has been proposed in [35]. Other, mainly preliminary, studies have approached the obstacle detection problem for the safe navigation of VCP as a 3D problem by using images along with depth information and enhancing the performance by exploiting the capabilities of CNN models [36][37][38].…”

Section: Obstacle Detectionmentioning

confidence: 99%

See 3 more Smart Citations

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

Dimas

Diamantis

Kalozoumis

et al. 2020

Sensors

Self Cite

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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 Detection Resultsmentioning

confidence: 99%

Section: Obstacle Detection Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Obstacle Detectionmentioning

confidence: 99%

See 2 more Smart Citations

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

Dimas

Diamantis

Kalozoumis

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Regarding the support of individuals with disabilities and especially the visually impaired individuals (VIIs), smart wearable assistive systems have been proposed [6], including object detection [7] and text recognition systems [8,9]. However, even though various wearable assistive systems have been developed for safe navigation of VIIs [6], most of them focus on obstacle detection and avoidance [10][11][12]. The majority of them have been applied mainly in indoor environments, whereas only a few of them address RP tasks [13,14].…”

Section: Route Planning and Smart Assistive Systems For Individuals Wmentioning

confidence: 99%

A route planning framework for smart wearable assistive navigation systems

Ntakolia

Iakovidis

2021

SN Appl. Sci.

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Route planning (RP) enables individuals to navigate in unfamiliar environments. Current RP methodologies generate routes that optimize criteria relevant to the traveling distance or time, whereas most of them do not consider personal preferences or needs. Also, most of the current smart wearable assistive navigation systems offer limited support to individuals with disabilities by providing obstacle avoidance instructions, but often neglecting their special requirements with respect to the route quality. Motivated by the mobility needs of such individuals, this study proposes a novel RP framework for assistive navigation that copes these open issues. The framework is based on a novel mixed 0–1 integer nonlinear programming model for solving the RP problem with constraints originating from the needs of individuals with disabilities; unlike previous models, it minimizes: (1) the collision risk with obstacles within a path by prioritizing the safer paths; (2) the walking time; (3) the number of turns by constructing smooth paths, and (4) the loss of cultural interest by penalizing multiple crossovers of the same paths, while satisfying user preferences, such as points of interest to visit and a desired tour duration. The proposed framework is applied for the development of a system module for safe navigation of visually impaired individuals (VIIs) in outdoor cultural spaces. The module is evaluated in a variety of navigation scenarios with different parameters. The results demonstrate the comparative advantage of our RP model over relevant state-of-the-art models, by generating safer and more convenient routes for the VIIs.

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User-centered system design for assisted navigation of visually impaired individuals in outdoor cultural environments

Ntakolia

Dimas

Iakovidis

2020

Univ Access Inf Soc

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Obstacle Detection Based on Generative Adversarial Networks and Fuzzy Sets for Computer-Assisted Navigation

Cited by 8 publications

References 13 publications

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

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

A route planning framework for smart wearable assistive navigation systems

User-centered system design for assisted navigation of visually impaired individuals in outdoor cultural environments

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