Efficient stairs detection algorithm Assisted navigation for vision impaired people

Carbonara, Sara; Guaragnella, Cataldo

doi:10.1109/inista.2014.6873637

Cited by 15 publications

(3 citation statements)

References 7 publications

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“…[16] regards stair edges arranged in parallel from bottom to top as features, and the upstairs/downstairs labels are classified by a support vector machine (SVM). [17] creatively regards the stair structure as a periodic signal in the spatial domain, and its period is the distance between two continuous edges. Then, the 2D fast Fourier transform (FFT) is applied to transform the observed signal to the frequency domain to obtain an image that contains only the edges of stairs.…”

Section: Traditional Methodsmentioning

confidence: 99%

Deep Leaning-Based Ultra-Fast Stair Detection

Wang¹,

Pei²,

Qiu³

et al. 2022

Preprint

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Staircases are some of the most common building structures in urban environments. Stair detection is an important task for various applications, including the environmental perception of exoskeleton robots, humanoid robots, and rescue robots and the navigation of visually impaired people. Most existing stair detection algorithms have difficulty dealing with the diversity of stair structure materials, extreme light and serious occlusion. Inspired by human perception, we propose an end-to-end method based on deep learning. Specifically, we treat the process of stair line detection as a multitask involving coarsegrained semantic segmentation and object detection. The input images are divided into cells, and a simple neural network is used to judge whether each cell contains stair lines. For cells containing stair lines, the locations of the stair lines relative to each cell are regressed. Extensive experiments on our dataset show that our method can achieve high performance in terms of both speed and accuracy. A lightweight version can even achieve 300+ frames per second with the same resolution. Our code and dataset will be soon available at GitHub.

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Section: Traditional Methodsmentioning

confidence: 99%

Deep Leaning-Based Ultra-Fast Stair Detection

Wang¹,

Pei²,

Qiu³

et al. 2022

Preprint

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“…Many existing approaches extract stair edges from RGB images to detect staircases [23][24][25]. Some use depth images to include range information and make the staircase detector model more robust [15,26].…”

Section: Staircase Detectionmentioning

confidence: 99%

Vision-Based Recognition of Human Motion Intent during Staircase Approaching

Islam

Haque

Imtiaz

et al. 2023

Sensors

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Walking in real-world environments involves constant decision-making, e.g., when approaching a staircase, an individual decides whether to engage (climbing the stairs) or avoid. For the control of assistive robots (e.g., robotic lower-limb prostheses), recognizing such motion intent is an important but challenging task, primarily due to the lack of available information. This paper presents a novel vision-based method to recognize an individual’s motion intent when approaching a staircase before the potential transition of motion mode (walking to stair climbing) occurs. Leveraging the egocentric images from a head-mounted camera, the authors trained a YOLOv5 object detection model to detect staircases. Subsequently, an AdaBoost and gradient boost (GB) classifier was developed to recognize the individual’s intention of engaging or avoiding the upcoming stairway. This novel method has been demonstrated to provide reliable (97.69%) recognition at least 2 steps before the potential mode transition, which is expected to provide ample time for the controller mode transition in an assistive robot in real-world use.

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“…From the perspective of requirement, 'accessibility', 'mobility', and 'wearable' are among the most popular keywords. The specific requirements of the system include: (1) detecting, recognizing, and avoiding objects [42,43]; (2) situational awareness [44]; (3) smartphone-based [45] mobile applications [46]; and (4) audio [47] and haptic feedbacks [48]. Furthermore, it can be seen in Figure 7 that the research hotspots in the last three years are CNN, assistive wearable devices, facial recognition in video streaming, infrared sensors, object recognition, and deep learning.…”

Section: Primary Topics and Research Hotspots By Keyword Co-occurrencementioning

confidence: 99%

A Bibliometric Narrative Review on Modern Navigation Aids for People with Visual Impairment

et al. 2021

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The innovations in the field of specialized navigation systems have become prominent research topics. As an applied science for people with special needs, navigation aids for the visually impaired are a key sociotechnique that helps users to independently navigate and access needed resources indoors and outdoors. This paper adopts the informetric analysis method to assess the current research and explore trends in navigation systems for the visually impaired based on bibliographic records retrieved from the Web of Science Core Collection (WoSCC). A total of 528 relevant publications from 2010 to 2020 were analyzed. This work answers the following questions: What are the publication characteristics and most influential publication sources? Who are the most active and influential authors? What are their research interests and primary contributions to society? What are the featured key studies in the field? What are the most popular topics and research trends, described by keywords? Additionally, we closely investigate renowned works that use different multisensor fusion methods, which are believed to be the bases of upcoming research. The key findings of this work aim to help upcoming researchers quickly move into the field, as they can easily grasp the frontiers and the trend of R&D in the research area. Moreover, we suggest the researchers embrace smartphone-based agile development, as well as pay more attention to phone-based prominent frameworks such as ARCore or ARKit, to achieve a fast prototyping for their proposed systems. This study also provides references for the associated fellows by highlighting the critical junctures of the modern assistive travel aids for people with visual impairments.

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Efficient stairs detection algorithm Assisted navigation for vision impaired people

Cited by 15 publications

References 7 publications

Deep Leaning-Based Ultra-Fast Stair Detection

Deep Leaning-Based Ultra-Fast Stair Detection

Vision-Based Recognition of Human Motion Intent during Staircase Approaching

A Bibliometric Narrative Review on Modern Navigation Aids for People with Visual Impairment

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