Video-based assistive aid for blind people using object recognition in dissimilar frames

Jabnoun, Hanen; Benzarti, Faouzi; Nicolier, Frédéric Morain; Amiri, Hamid

doi:10.1504/ijaip.2019.102967

Cited by 3 publications

(2 citation statements)

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“…Furthermore, Jabnoun et al, in [42], described a visual tool for VI people, based on an object recognition algorithm allowing to determine the dissimilarity between video frames; specifically, the algorithm employs the Real-Valued Local Dissimilarity Map (RVLDM) method, as a measure of frames' dissimilarity, and the Scale-Invariant Features Transform (SIFTS) keypoints extraction, for determining the objects depicted in different frames. By comparing the proposed method with similar visual substitution approaches, optimal performances have been demonstrated, in terms of computational speed in different operative conditions, such as different point-of-view, presence of occlusion, frame rotation, and different illumination.…”

Section: Overview Of Applications and Innovative Methods For Visual Rmentioning

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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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: Overview Of Applications and Innovative Methods For Visual Rmentioning

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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“…Due to the rapid development of convolutional neural networks, deep learning based methods have not only achieved remarkable results in the field of computer vision [15,18], but also been rapidly developed and widely applied in the fields of atmospheric monitoring [6,44], wireless transmission [45][46][47] and health assistance [16,19,26,32]. And in the field of industrial safety monitoring, the use of deep learning based object detection methods to realize the automated detection of helmets has become one of the urgent problems to be solved in the current industrial safety management.…”

Section: Introductionmentioning

confidence: 99%

Helmet Detection Based on Context Enhancement Pyramid Under Surveillance Images

Xu,

2024

ITC

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Helmet detection is of great significance for realizing the automated management of industrial safety. To address the problem that existing object detection methods have insufficient ability to detect helmet small objects under surveillance images, this paper proposes a helmet detection based on context enhancement pyramid under surveillance images to realize the automatic detection task of helmet objects. The method helps the network improve position localization for small-scale helmet objects by adding a high-resolution detection layer to YOLOv5. Also, the proposed context enhancement pyramid reduces the semantic differences between different scale features and generates rich contextual features to enhance the network’s discriminative learning ability for helmet small object features. In addition, the proposed multi-scale attention module improves thefeature fusion effect in the pyramid network to further capture multi-scale features and expand the receptive field to enhance the network’s detection precision of helmet objects under surveillance images. The experimental analysis shows that the proposed method has good detection effect compared to existing object detection methods on the Safety Helmet Wearing Dataset (SHWD) as well as the customized dataset.

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Visually Impaired People Positioning Assistance System Using Artificial Intelligence

et al. 2023

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Video-based assistive aid for blind people using object recognition in dissimilar frames

Cited by 3 publications

References 0 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

Helmet Detection Based on Context Enhancement Pyramid Under Surveillance Images

Visually Impaired People Positioning Assistance System Using Artificial Intelligence

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