Detecting Traversable Area and Water Hazards for the Visually Impaired with a pRGB-D Sensor

Yang, Kailun; Wang, Kaiwei; Cheng, Ruiqi; Hu, Weijian; Huang, Xiao; Bai, Jian

doi:10.3390/s17081890

Cited by 37 publications

(44 citation statements)

References 51 publications

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“…To overcome the range limitations of RGB-D cameras, some authors have augmented them using additional sensors such as ultrasonic sensors and millimeter-wave radars [ 15 , 16 ]. Furthermore, water hazard detection [ 17 ] and small obstacle avoidance [ 18 , 19 ] have also been addressed.…”

Section: Related Workmentioning

confidence: 99%

Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video

Martínez

Yang

Constantinescu

et al. 2020

Sensors

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The current COVID-19 pandemic is having a major impact on our daily lives. Social distancing is one of the measures that has been implemented with the aim of slowing the spread of the disease, but it is difficult for blind people to comply with this. In this paper, we present a system that helps blind people to maintain physical distance to other persons using a combination of RGB and depth cameras. We use a real-time semantic segmentation algorithm on the RGB camera to detect where persons are and use the depth camera to assess the distance to them; then, we provide audio feedback through bone-conducting headphones if a person is closer than 1.5 m. Our system warns the user only if persons are nearby but does not react to non-person objects such as walls, trees or doors; thus, it is not intrusive, and it is possible to use it in combination with other assistive devices. We have tested our prototype system on one blind and four blindfolded persons, and found that the system is precise, easy to use, and amounts to low cognitive load.

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Section: Related Workmentioning

confidence: 99%

Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video

Martínez

Yang

Constantinescu

et al. 2020

Sensors

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“…Obstacles are detected by sensors such as ultrasonic sensors [13][14][15], RGB-D sensors [16][17][18], and stereo cameras [19,20] attached to shoes, heads, and white canes. UltraCane/BatCane is a system that detects obstacles using only a single ultrasonic sensor attached to a white cane [15].…”

Section: Obstacle Detection and Identificationmentioning

confidence: 99%

“…There is research to detect human faces using Android [21]. In related research, RGB-D sensors were used to detect puddles and stereo sound feedback was used to guide the visually impaired people to the safe route [17]. Co-Robotic Cane [16] was proposed, which uses RGB-D cameras to estimate device posture and identify objects in indoor environments.…”

Section: Obstacle Detection and Identificationmentioning

confidence: 99%

Object Identification and Safe Route Recommendation Based on Human Flow for the Visually Impaired

Kajiwara

Kimura

2019

Sensors

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It is difficult for visually impaired people to move indoors and outdoors. In 2018, world health organization (WHO) reported that there were about 253 million people around the world who were moderately visually impaired in distance vision. A navigation system that combines positioning and obstacle detection has been actively researched and developed. However, when these obstacle detection methods are used in high-traffic passages, since many pedestrians cause an occlusion problem that obstructs the shape and color of obstacles, these obstacle detection methods significantly decrease in accuracy. To solve this problem, we developed an application “Follow me!”. The application recommends a safe route by machine learning the gait and walking route of many pedestrians obtained from the monocular camera images of a smartphone. As a result of the experiment, pedestrians walking in the same direction as visually impaired people, oncoming pedestrians, and steps were identified with an average accuracy of 0.92 based on the gait and walking route of pedestrians acquired from monocular camera images. Furthermore, the results of the recommended safe route based on the identification results showed that the visually impaired people were guided to a safe route with 100% accuracy. In addition, visually impaired people avoided obstacles that had to be detoured during construction and signage by walking along the recommended route.

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“…Despite of high accuracy, the artificial neural network with complex structure needs to be pre-trained in many scenes before use and requires high computing power. For hardware-based methods, some studies have used various optical sensors such as laser radar [15], infrared camera [1], stereo camera [16,17], and polarized camera [16,18,19] to easily realize water hazard detection based on the optical characteristics of waters [20]. These methods are still difficult to popularize in applications due to the cost and equipment complexity.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of River Scenes Based on Water Surface Reflection Mechanism

Lin

Zhu

et al. 2020

Applied Sciences

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Segmentation of a river scene is a representative case of complex image segmentation. Different from road segmentation, river scenes often have unstructured boundaries and contain complex light and shadow on the water’s surface. According to the imaging mechanism of water pixels, this paper designed a water description feature based on a multi-block local binary pattern (MB-LBP) and Hue variance in HSI color space to detect the water region in the image. The improved Local Binary Pattern (LBP) feature was used to recognize the water region and the local texture descriptor in HSI color space using Hue variance was used to detect the shadow area of the river surface. Tested on two data sets including simple and complex river scenes, the proposed method has better segmentation performance and consumes less time than those of two other widely used methods.

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Detecting Traversable Area and Water Hazards for the Visually Impaired with a pRGB-D Sensor

Cited by 37 publications

References 51 publications

Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video

Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video

Object Identification and Safe Route Recommendation Based on Human Flow for the Visually Impaired

Segmentation of River Scenes Based on Water Surface Reflection Mechanism

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