Indoor objects detection and recognition for an ICT mobility assistance of visually impaired people

Afif, Mouna; Ayachi, Riadh; Pissaloux, Edwige; Said, Yahia; Atri, Mohamed

doi:10.1007/s11042-020-09662-3

Cited by 48 publications

(26 citation statements)

References 38 publications

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“…For Edge detection, we found two algorithms methods; canny edge detector that is used to detect doors (Sivan & Darsan, 2016) and line segment detector, which is also used to detect doors with an accuracy rate of up to 93.2% for the ImageNet dataset (Talebi & Vafaei, 2018). For objects and obstacle detection, we found three algorithms; first is CNN to recognize the color and sign of traffic got mAP of 96% % (Li, Cui, Rizzo, Wong, & Fang, 2020); in another research, CNN is also used to detect objects, but not accurate for multi objects in one scene, so they implemented RCNN (Balasuriya, Lokuhettiarachchi, Ranasinghe, Shiwantha, & Jayawardena, 2017), second is YOLOv1 used to detect objects and obstacles and the detection rate is up to 89% for all kind of obstacles (Mocanu, Tapu, & Zaharia, 2017), and third is YOLOv3 also used to detect objects, and the mAP is 73.19% (Afif, Ayachi, Pissaloux, Said, & Atri, 2020), and in the other research the accuracy rate is up to 95.19% (Joshi, Yadav, Dutta, & Travieso-Gonzalez, 2020) and 92% (Mahmud, Sourave, Islam, Lin, & Kim, 2020). For image classification, we found two algorithms; the first is KNN to match the descriptor extracted (Elmannai & Elleithy, 2018) and SVM to produce a category label for a scene (Zientara, et al, Feb. 2017).…”

Section: Proposed Algorithm In CV Usedmentioning

confidence: 99%

“…We found an eligible r3esearch that help VIP to recognize symbols in toilets, pharmacies, and trains (Dahiya, Issac, Dutta, Říha, & Kříž, 4-6 July 2018), while another studies help2ed VIP to recognize signs-based-text, then the text will be converted into sound (Jiang, Gonnot, Yi, & Saniie, 14-17 May 2017) (Sivan & Darsan, 2016). To detect objects, two studies try to help VIP to detect moving and not moving objects in the outdoor area (Mocanu, Tapu, & Zaharia, 2017) (Joshi, Yadav, Dutta, & Travieso-Gonzalez, 2020), while another study proposed a system that can detect objects in the indoor area (Sivan & Darsan, 2016) (Afif, Ayachi, Pissaloux, Said, & Atri, 2020). Another study helped to assist VIP by detecting surrounding obstacles so that VIP can navigate their own way with the help of audio feedback (Elmannai & Elleithy, 2018) (Duh, Sung, Chiang, Chang, & Chen, 2020).…”

Section: Supported Tasksmentioning

confidence: 99%

“…RS9 (Talebi & Vafaei, 2018) Mehdi Talebi, Abbas Vafaei, and Object Detection RS5 (Mocanu, Tapu, & Zaharia, 2017), RS6 (Afif, Ayachi, Pissaloux, Said, & Atri, 2020), RS7 (Joshi, Yadav, Dutta, & Travieso-Gonzalez, 2020), RS8 (Duh, Sung, Chiang, Chang, & Chen, 2020), RS10 (Elmannai & Elleithy, 2018), RS11 (Mahmud, Sourave, Islam, Lin, & Kim, 2020), RS12 (Zientara, et al, Feb. 2017), RS13 (Balasuriya, Lokuhettiarachchi, Ranasinghe, Shiwantha, & Jayawardena, 2017) Door Detection RS2 (Sivan & Darsan, 2016), RS9 (Talebi & Vafaei, 2018) Traffic Light Detection RS4 (Li, Cui, Rizzo, Wong, & Fang, 2020) Object Tracking RS5 (Mocanu, Tapu, & Zaharia, 2017) Navigation RS8 (Duh, Sung, Chiang, Chang, & Chen, 2020), RS11 (Mahmud, Sourave, Islam, Lin, & Kim, 2020)…”

Section: Appendixmentioning

confidence: 99%

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Computer Vision for Supporting Visually Impaired People: A Systematic Review

Anthony

Kusnadi

2021

EMACS Journal

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Globally around the world in 2010, the number of people of all ages visually impaired is estimated to be 285 million, of whom 39 million are blind according to the study of World Health Organization (Global Data on Visual Impairments, 2010). Visual impairment has a significant impact on individuals’ quality of life, including their ability to work and to develop personal relationships. Almost half (48 %) of the visually impaired feel “moderately” or “completely” cut off from people and things around them (Hakobyan, Lumsden, O’Sullivan, & Bartlett, 2013). We believe that technology has the potential to enhance individuals’ ability to participate fully in societal activities and to live independently. So, in this paper we focused to presents a comprehensive literature review about different algorithms of computer vision for supporting blind/vision impaired people, different devices used and the supported tasks. From the 13 eligible papers, we found positive effects of the use of computer vision for supporting visually impaired people. These effects included: the detection of obstacles, objects, door and text, traffic lights, sign detections and navigation. But the biggest challenge for developers now is to increase the speed of time and improve its accuracy, and we expect the future will have a complete package or solution where blind or vision impaired people will get all the solution together (i.e., map, indoor-outdoor navigation, object recognition, obstacle recognition, person recognition, human crowd behavior, crowd human counting, study/reading, entertainment etc.) in one software and in hand-held devices like android or any handy devices.

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Section: Proposed Algorithm In CV Usedmentioning

confidence: 99%

Section: Supported Tasksmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

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Computer Vision for Supporting Visually Impaired People: A Systematic Review

Anthony

Kusnadi

2021

EMACS Journal

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“…The system uses Microsoft HoloLens to learn the geometry layout of the surroundings, which is necessary to plan feasible paths [11][12][13]. Edwige Pissaloux recently used a framework based on deep convolutional neural networks (Deep CNN) to detect indoor targets [14,15], an essential component for intelligence assistive systems. Cang Ye focused on guiding robots [16,17], while Bogdan Mocanu mainly studied mobile facial recognition, which is supposed to support the assistive system [18,19].…”

Section: Most Active and Influential Authors By Co-authorship And Co-citationshipmentioning

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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“…With the big development of image processing techniques, the performance of computer vision applications has also been improved. The recent Deep Learning techniques [3] has been widely deployed for solving many applications such as object detection [4], traffic sign detection [5], indoor object detection and recognition [6,7], pedestrian detection [8], and images segmentation [9]. Most image processing applications are based on Convolutional Neural Networks (CNN) [10].…”

Section: Introductionmentioning

confidence: 99%

Desertification Detection in Makkah Region based on Aerial Images Classification

Said¹,

Barr²,

Saidani³

et al. 2022

Computer Systems Science and Engineering

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Desertification has become a global threat and caused a crisis, especially in Middle Eastern countries, such as Saudi Arabia. Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification. The vegetation area in Makkah has been damaged because of desertification through wind, floods, overgrazing, and global climate change. The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area. In this paper, we propose an automatic desertification detection system based on Deep Learning techniques. Aerial images are classified using Convolutional Neural Networks (CNN) to detect land state variation in real-time. CNNs have been widely used for computer vision applications, such as image classification, image segmentation, and quality enhancement. The proposed CNN model was trained and evaluated on the Arial Image Dataset (AID). Compared to state-of-the-art methods, the proposed model has better performance while being suitable for embedded implementation. It has achieved high efficiency with 96.47% accuracy. In light of the current research, we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.

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Indoor objects detection and recognition for an ICT mobility assistance of visually impaired people

Cited by 48 publications

References 38 publications

Computer Vision for Supporting Visually Impaired People: A Systematic Review

Computer Vision for Supporting Visually Impaired People: A Systematic Review

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

Desertification Detection in Makkah Region based on Aerial Images Classification

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