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

Abstract

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.

Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video

“…Specifically, they used the OpenPose model [ 25 ] to detect human skeletons using a RGB-D camera, where the depth maps enabled the localization of the pedestrian’s skeleton trunks for human flow avoidance. Recently, Dimas et al [ 26 ] also devised a pair of smart glasses based on an RGB-D sensor and performed the uncertainty-aware modeling of obstacle risk assessment for the visually challenged. While products such as the Bat Orientation Guide [ 27 ] allows people or moving objects to be followed at a constant distance, they cannot handle the social distancing problem in unstructured environments.…”

Sensors, Signal and Image Processing in Biomedicine and Assisted Living

“…To assist outdoor navigation of visually challenged individuals, a novel wearable visual perception system (VPS) is presented in [ 10 ]. By wearing that system, which is equipped with a stereoscopic RGB camera that can assess depth, the users receive information enabling them to avoid obstacles and safely navigate in outdoor environments.…”

“…This special issue gathers a broad range of novel contributions on sensors, systems, and signal/image processing methods for biomedicine and assisted living. These include methods for heart, sleep and vital sign measurement [ 1 , 2 , 3 , 4 , 5 ]; human motion-related signal analysis in the context of rehabilitation and tremor assessment [ 6 , 7 , 8 ]; assistive systems for color deficient and visually challenged individuals, as well as for wheelchair control by people with motor disabilities [ 9 , 10 , 11 , 12 ]; and, image and video-based diagnostic systems [ 13 , 14 , 15 , 16 ].…”

ASML: Algorithm-Agnostic Architecture for Scalable Machine Learning

“…The flexibility of such services is limited, as while it is relatively easy to get started, it is difficult to efficiently incorporate ML models based on novel components, such as the fuzzy pooling layer proposed in [5], or complex ML-based data-processing pipelines, such as pipelines that include image preprocessing, integration of multiple heterogeneous ML algorithms with bidirectional data communication. Such pipelines are frequently met in state-ofthe-art pattern analysis applications spanning a variety of domains e.g., web content perception [6], obstacle detection and navigation for robotics [7] and assistive technologies [8], real-time analysis of medical image sequences during brain surgery [9] and gastrointestinal (GI) endoscopy [10]. However, their deployment in a SaaS context, using current ML frameworks, is far from straightforward, especially when high-throughput capacity is required.…”

“…To evaluate the performance and the flexibility of the proposed system architecture, we conducted experiments for two SaaS use case scenarios, where pattern recognition is provided as a cloud service. The first use case addresses the complex task of multi-user obstacle avoidance in the context of visually impaired navigation, using a state-ofthe-art obstacle avoidance framework [8]. The second use case includes synchronous and asynchronous task execution in the context of abnormality detection in gastrointestinal endoscopy images [10].…”