Real-Time Indoor Staircase Detection on Mobile Devices

Ciobanu, Amelia; Morar, Anca; Moldoveanu, Florica; Petrescu, Lucian; Ferche, Oana; Moldoveanu, Alin

doi:10.1109/cscs.2017.46

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…A direct comparison with systems already reported in the literature targeting the assistance of the visually impaired is not evident since most of them miss to report their mAPs [29,30,32]. Other object recognition works [44,68,69] for different applications report comparable mAPs between 70% and 90% exploring other deep learning approaches, such as SSD (Single Shot MultiBox Detector), YOLO (You Only Look Once), and R-FCN (Region-based Fully Convolutional Networks) with the use of other image databases for training, such as the PASCAL VOC (Visual Object Classes), SUN (Scene UNderstanding), and the KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) one.…”

Section: Results Discussionmentioning

confidence: 95%

“…Jabnoun and colleagues reported in [31] an object recognition system based on SIFT (Scale Invariant Features Transform) and SURF algorithms to assist VI people during navigation. Ciobanu et al introduced, in [32], a method for detecting indoor staircases by employing IMU (inertial measurement unit) sensors and processing depth images in order to aid VI people in unfamiliar environments.…”

Section: State Of the Art On Innovative Assistive Technology Devices mentioning

confidence: 99%

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Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

et al. 2020

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This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.

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Section: Results Discussionmentioning

confidence: 95%

Section: State Of the Art On Innovative Assistive Technology Devices mentioning

confidence: 99%

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

et al. 2020

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“…Ciabanou et al [ 101 ] developed a system to detect indoor staircases with the help of an RGB-D camera. The algorithm is based on clustering patches from the normal map.…”

Section: Resultsmentioning

confidence: 99%

Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Leiva

Jaén-Vargas

Codina

et al. 2021

Sensors

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A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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“…The second group focuses on the detection of the stairs themselves. 13,14,16,21,[24][25][26][27][28][29][30] The choice of sensor in this work-a LIDAR as opposed to a camera, 13,14,24 or a depth sensor 16,21,[25][26][27][28][29][30] differentiates how the sensory information about the stairs is generated and how this information is processed to accomplish the detection from these approaches.…”

Section: Contributions and Related Workmentioning

confidence: 99%

Autonomous Stairwell Ascent

Ilhan¹,

Johnson²,

Koditschek³

2019

Robotica

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SummaryThis paper documents autonomous multi-floor stairwell ascent by a legged robot. This is made possible through empirically deployed sequential composition of several reactive controllers, with perceptually triggered transitions. This composition relies on simplified assumptions regarding the robot’s sensory capabilities, its level of mobility, and the environment it operates in. The discrepancies between these assumptions and the physical reality are capably handled by the intrinsic motor competence of the robot. This behavior is implemented on the legged RHex platform and experiments spanning 10 different stairwells with various challenges are conducted.

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Real-Time Indoor Staircase Detection on Mobile Devices

Cited by 16 publications

References 8 publications

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Autonomous Stairwell Ascent

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