Seeing Eye Phone: a smart phone-based indoor localization and guidance system for the visually impaired

Zhang, Dong; Lee, Dah-Jye; Taylor, Brandon

doi:10.1007/s00138-013-0575-0

Cited by 16 publications

(6 citation statements)

References 11 publications

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“…Zhang et al [14] proposed a method of indoor localisation using still images captured at regular intervals from a smart-phone worn via a lanyard. The goal of the approach was to assist navigation throughout a familiar environment for those with impaired vision.…”

Section: Related Workmentioning

confidence: 99%

Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor

Shewell

Medina-Quero

Espinilla

et al. 2016

Int J Communication

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This paper presents a comparison between algorithms (Oriented FAST and Rotated BRIEF (ORB) andAruco) for the detection of fiducial markers placed throughout a smart environment. A series of activities of daily living (ADL) were conducted while monitoring a first-person perspective of the situation; this was achieved through the usage of the Google Glass platform. Fiducial markers were employed, as a means to assist with the detection of specific objects of interest, within the environment. Each marker was assigned unique Identification (ID) and was used to identify the object. Three activities were performed by a participant within the environment. On subsequent trials of the solution, lighting conditions were modified to assess fiducial marker detection rates on a frame-by-frame basis. This paper presents the results from this investigation, detailing performance measure for each object detected under various lighting conditions, motion blur and distance from the objects. An intelligent system was developed to specifically consider distance estimation in order to aid with the filtering out of false interactions. A linear filtering method was applied along with a fuzzy membership function to estimate the degree of user interaction, which assists in removing false positives generated by the occupant. The intelligent system returns an average precision, recall and an F-Measure of 0.99, 0.62 and 0.49, respectively.

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Section: Related Workmentioning

confidence: 99%

Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor

Shewell

Medina-Quero

Espinilla

et al. 2016

Int J Communication

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show abstract

“…Zhang et al [ 33 ] proposed a method of indoor location using still images captured at intervals from a smart-phone worn on a lanyard. This system has the goal of assisting those with impaired vision to navigate within an indoor environment.…”

Section: Related Workmentioning

confidence: 99%

Indoor localisation through object detection within multiple environments utilising a single wearable camera

et al. 2016

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The recent growth in the wearable sensor market has stimulated new opportunities within the domain of Ambient Assisted Living, providing unique methods of collecting occupant information. This approach leverages contemporary wearable technology, Google Glass, to facilitate a unique first-person view of the occupants immediate environment. Machine vision techniques are employed to determine an occupant’s location via environmental object detection. This method provides additional secondary benefits such as first person tracking within the environment and lack of required sensor interaction to determine occupant location. Object recognition is performed using the Oriented Features from Accelerated Segment Test and Rotated Binary Robust Independent Elementary Features algorithm with a K-Nearest Neighbour matcher to match the saved key-points of the objects to the scene. To validate the approach, an experimental set-up consisting of three ADL routines, each containing at least ten activities, ranging from drinking water to making a meal were considered. Ground truth was obtained from manually annotated video data and the approach was previously benchmarked against a common method of indoor localisation that employs dense sensor placement in order to validate the approach resulting in a recall, precision, and F-measure of 0.82, 0.96, and 0.88 respectively. This paper will go on to assess to the viability of applying the solution to differing environments, both in terms of performance and along with a qualitative analysis on the practical aspects of installing such a system within differing environments.

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“…In [1,4] the authors propose systems that visually detect known makers placed into the scene so to guarantee accurate localization of the user: Though effective, these approaches are limited to work only in previously structured environments. Zhang et al [18] developed a smartphone-based system to visually localize the user. A drawback of this method is that images of the environment have to be previously collected and mapped, and heavy 3D computations must be run on a remote server.…”

Section: Related Workmentioning

confidence: 99%

Smartphone-Based Obstacle Detection for the Visually Impaired

Caldini

Fanfani

Colombo

2015

Lecture Notes in Computer Science

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Abstract.One of the main problems that visually impaired people have to deal with is moving autonomously in an unknown environment. Currently, the most used autonomous walking aid is still the white can. Though in the last few years more technological devices have been introduced, referred to as electronic travel aids (ETAs). In this paper, we present a novel ETA based on computer vision. Exploiting the hardware and software facilities of a standard smartphone, our system is able to extract a 3D representation of the scene and detect possible obstacles. To achieve such a result, images are captured by the smartphone camera and processed with a modified Structure from Motion algorithm that takes as input also information from the built-in gyroscope. Then the system estimates the ground-plane and labels as obstacles all the structures above it. Results on indoor and outdoor test sequences show the effectiveness of the proposed method.

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Seeing Eye Phone: a smart phone-based indoor localization and guidance system for the visually impaired

Cited by 16 publications

References 11 publications

Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor

Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor

Indoor localisation through object detection within multiple environments utilising a single wearable camera

Smartphone-Based Obstacle Detection for the Visually Impaired

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