Blind path obstacle detector using smartphone camera and line laser emitter

Saffoury, Rimon; Blank, Peter; Seßner, Julian; Groh, Benjamin H.; Martindale, Christine F.; Dorschky, Eva; Franke, Jöerg; Eskofier, Bjoern M.

doi:10.1109/tishw.2016.7847770

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…While it does not mean a drawback on the system, it is just inconvenient to setup at times as it requires expertise or comprehension of the developed system, whereas utilizing a depth sensing application with an inclusive user interface on a smartphone only is a more accessible option. Smartphone-based navigational aids have already existed for a long time, with examples such as an obstacle detection and classification system using points of interest [31], obstacle collision detection using emitted acoustic signal beep from a microphone on a smartphone [32], obstacle avoidance using smartphone camera and emitted laser light triangulation [33], and many more. These studies provide promising results as a navigational aid to VIB people; however, these implementations have inconsistencies due to hardware and software limitations.…”

Section: Usability Experience Of the Mobile Applicationmentioning

confidence: 99%

A Smartphone-Based Mobility Assistant Using Depth Imaging for Visually Impaired and Blind

2022

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The means of assisting visually impaired and blind (VIB) people when travelling usually relies on other people. Assistive devices have been developed to assist in blind navigation, but many technologies require users to purchase more devices and they lack flexibility, thus making it inconvenient for VIB users. In this research, we made use of a mobile phone with a depth camera function for obstacle avoidance and object recognition. It includes a mobile application that is controlled using simple voice and gesture controls to assist in navigation. The proposed system gathers depth values from 23 coordinate points that are analyzed to determine whether an obstacle is present in the head area, torso area, or ground area, or is a full body obstacle. In order to provide a reliable warning system, the research detects outdoor objects within a distance of 1.6 m. Subsequently, the object detection function includes a unique interactable feature that enables interaction with the user and the device in finding indoor objects by providing an audio and vibration feedback, and users were able to locate their desired objects more than 80% of the time. In conclusion, a flexible and portable system was developed using a depth camera-enabled mobile phone for use in obstacle detection without the need to purchase additional hardware devices.

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Section: Usability Experience Of the Mobile Applicationmentioning

confidence: 99%

A Smartphone-Based Mobility Assistant Using Depth Imaging for Visually Impaired and Blind

2022

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“…In first layer, the white cane orientation is tracked while in second layer, the 2D position of the person (holding white cane) is tracked. Another system proposed by Saffoury et al [5] consist of two hardware components, a laser pointer and an android based smartphone. To measure distance of objects in environment, image processing based techniques are used for obstacle avoidance.…”

Section: Related Workmentioning

confidence: 99%

Obstacle Detection in Indoor Environment for Visually Impaired Using Mobile Camera

Rahman

Ullah

2018

J. Phys.: Conf. Ser.

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Abstract. Obstacle detection can improve the mobility as well as the safety of visually impaired people. In this paper, we present a system using mobile camera for visually impaired people. The proposed algorithm works in indoor environment and it uses a very simp le technique of using few p re-stored floor images. In indoor environ ment all unique floor types are considered and a single image is stored for each unique floor type. These floor images are considered as reference images. The algorithm acquires an input image frame and then a region of interest is selected and is scanned for obstacle using pre-stored floor images. The algorithm compares the present frame and the next frame and co mpute mean square error of the two frames. If mean square error is less than a threshold value α then it means that there is no obstacle in the next frame. If mean square error is greater than α then there are t wo possibilit ies; either there is an obstacle or the floor type is changed. In order to check if the floor is changed, the algorithm co mputes mean square error of next frame and all stored floor types. If minimu m of mean square error is less than a threshold value α then flour is changed otherwise there exist an obstacle. The proposed algorithm works in real-time and 96% accuracy has been achieved.

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“…Recently, there are numerous devices on the market that are used in our everyday life such as smart phones, tablets, and other handheld devices. Smart phones have been used in many modern applications [1][2][3][4][5]. In commercial applications, smart phones are widely used to make banking transactions and money transfer.…”

Section: Introductionmentioning

confidence: 99%

“…Depth sensors are contact free and avoid some of the pitfalls caused by variations in lighting and background conditions that are typically associated with images from conventional cameras. In [3], a smart system has been proposed to help blind people avoiding obstacles while walking. A laser pointer and an android smart phone are used with an image processing algorithm to avoid obstacles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Smart Phones Inductive Charging Station System

Richardson¹

2018

IJCDS

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In today's technology market, more and more devices are moving towards wireless charging. For example, one of the flagship companies, Apple, recently announced that they would begin to offer wireless charging in their devices. Besides Apple, many other companies such as Samsung, LG, HTC, and Sony already offer this feature to their customers. Many consumers have issues with the prices that come with replacing disfigured cables that result from daily use. In response to this, we would like to promote wireless charging through building an inductive charging station capable of detecting and charging multiple devices wirelessly at the same time. This would eliminate the need for charging cables, and the wear and tear that comes with them. This would also be more cost effective than buying multiple copies of the wireless chargers that are already on the market. The proposed design would be able to charge up to six devices simultaneously via a coil array and detection circuit. The design takes into consideration the dimensions of the current smart devices and moves to accommodate such devices. This device would also be cost effective so that acquiring would not be an issue, be it businesses, universities, or public spaces that take an interest. The designed wireless charging station cost approximately $173. It is designed to charge different smart phones with different dimensions. Practically, our station can fully charge smart phones approximately in same time as if using wired charging cables. The designed charging station is flexible and can be updated to charge bigger smart devices.

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Blind path obstacle detector using smartphone camera and line laser emitter

Cited by 14 publications

References 13 publications

A Smartphone-Based Mobility Assistant Using Depth Imaging for Visually Impaired and Blind

A Smartphone-Based Mobility Assistant Using Depth Imaging for Visually Impaired and Blind

Obstacle Detection in Indoor Environment for Visually Impaired Using Mobile Camera

Multiple Smart Phones Inductive Charging Station System

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