Advanced crosswalk detection for the Bionic Eyeglass

Radvanyi, Mihaly; Varga, Balázs; Karacs, Kristóf

doi:10.1109/cnna.2010.5430281

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…Wu et al [15] propose a block-based Hough transform approach that effectively identifies marked crosswalks in natural scene images, contributing to the development of robust detection methods. Radványi et al [16] introduce advanced crosswalk detection techniques tailored for the Bionic Eyeglass, offering enhanced functionality and usability for visually impaired users. Cao et al [17] present an image-based detection method specifically designed for pedestrian crossings, utilizing visual cues and patterns to identify these critical areas.…”

Section: Related Workmentioning

confidence: 99%

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Pedestrian Crossing Sensing Based on Hough Space Analysis to Support Visually Impaired Pedestrians

Yoshikawa

Premachandra

2023

Sensors

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There are many visually impaired people globally, and it is important to support their ability to walk independently. Acoustic signals and escort zones have been installed on pedestrian crossings for the visually impaired people to walk safely; however, pedestrian accidents, including those involving the visually impaired, continue to occur. Therefore, to realize safe walking for the visually impaired on pedestrian crossings, we present an automatic sensing method for pedestrian crossings using images from cameras attached to them. Because the white rectangular stripes that mark pedestrian crossings are aligned, we focused on the edges of these rectangular stripes and proposed a novel pedestrian crossing sensing method based on the dispersion of the slope of a straight line in Hough space. Our proposed method possesses unique characteristics that allow it to effectively handle challenging scenarios that traditional methods struggle with. It excels at detecting crosswalks even in low-light conditions during nighttime when illumination levels may vary. Moreover, it can detect crosswalks even when certain areas are partially obscured by objects or obstructions. By minimizing computational costs, our method achieves high real-time performance, ensuring efficient and timely crosswalk detection in real-world environments. Specifically, our proposed method demonstrates an impressive accuracy rate of 98.47%. Additionally, the algorithm can be executed at almost real-time speeds (approximately 10.5 fps) using a Jetson Nano small-type computer, showcasing its suitability as a wearable device.

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Section: Related Workmentioning

confidence: 99%

“…In order to overcome the limitations of existing methods, we focused on developing a detection method specifically designed for pedestrians to wear [15][16][17][18][19][20][21][22][23][24][25][26][27]. This innovative approach addresses the challenges associated with conventional methods.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Crossing Sensing Based on Hough Space Analysis to Support Visually Impaired Pedestrians

Yoshikawa

Premachandra

2023

Sensors

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“…Some of them, such as [8] and [9], have been implemented on a smartphone. Radvanyi et al [10] proposed a wearable device based on a neural network to detect ground plane in 2D images and then recognizing crosswalks. In [11], the 3D data obtained through a stereo vision system is processed applying the Hough transform in the 2D and 3D domain to detect crosswalks and stairs.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Crosswalk Recognition Through Point-Cloud Processing and Deep-Learning Suited to a Wearable Mobility Aid for the Visually Impaired

Poggi

Nanni

Mattoccia

2015

Lecture Notes in Computer Science

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In smart-cities, computer vision has the potential to dramatically improve the quality of life of people suffering of visual impairments. In this field, we have been working on a wearable mobility aid aimed at detecting in real-time obstacles in front of a visually impaired. Our approach relies on a custom RGBD camera, with FPGA on-board processing, worn as traditional eyeglasses and effective point-cloud processing implemented on a compact and lightweight embedded computer. This latter device also provides feedback to the user by means of an haptic interface as well as audio messages. In this paper we address crosswalk recognition that, as pointed out by several visually impaired users involved in the evaluation of our system, is a crucial requirement in the design of an effective mobility aid. Specifically, we propose a reliable methodology to detect and categorize crosswalks by leveraging on point-cloud processing and deep-learning techniques. The experimental results reported, on 10000+ frames, confirm that the proposed approach is invariant to head/camera pose and extremely effective even when dealing with large occlusions typically found in urban environments.

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RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons

Tian

2014

Computer Vision and Machine Learning With RGB-D Sensors

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Advanced crosswalk detection for the Bionic Eyeglass

Cited by 11 publications

References 11 publications

Pedestrian Crossing Sensing Based on Hough Space Analysis to Support Visually Impaired Pedestrians

Pedestrian Crossing Sensing Based on Hough Space Analysis to Support Visually Impaired Pedestrians

Crosswalk Recognition Through Point-Cloud Processing and Deep-Learning Suited to a Wearable Mobility Aid for the Visually Impaired

RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons

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