Next Article in Journal
3D Tracking via Shoe Sensing
Next Article in Special Issue
Calorimetry Minisensor for the Localised Measurement of Surface Heat Dissipated from the Human Body
Previous Article in Journal
Electrochemical Aptasensor for Myoglobin-Specific Recognition Based on Porphyrin Functionalized Graphene-Conjugated Gold Nanocomposites
Previous Article in Special Issue
A Vision-Based Approach for Building Telecare and Telerehabilitation Services
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(11), 1807; doi:10.3390/s16111807

When Ultrasonic Sensors and Computer Vision Join Forces for Efficient Obstacle Detection and Recognition

1,2,†
,
1,2,†,* and 1
1
ARTEMIS Department, Institut Mines-Télécom/Télécom SudParis, UMR CNRS MAP5 8145, 9 rue Charles Fourier, Évry 91000, France
2
Telecommunication Department, Faculty of ETTI, University Politehnica of Bucharest, Splaiul Independentei 313, Bucharest 060042, Romania
These authors contributed equally to this paper.
*
Author to whom correspondence should be addressed.
Academic Editors: Octavian Adrian Postolache, Alex Casson and Subhas Mukhopadhyay
Received: 30 August 2016 / Revised: 24 October 2016 / Accepted: 25 October 2016 / Published: 28 October 2016
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
View Full-Text   |   Download PDF [14141 KB, uploaded 28 October 2016]   |  

Abstract

In the most recent report published by the World Health Organization concerning people with visual disabilities it is highlighted that by the year 2020, worldwide, the number of completely blind people will reach 75 million, while the number of visually impaired (VI) people will rise to 250 million. Within this context, the development of dedicated electronic travel aid (ETA) systems, able to increase the safe displacement of VI people in indoor/outdoor spaces, while providing additional cognition of the environment becomes of outmost importance. This paper introduces a novel wearable assistive device designed to facilitate the autonomous navigation of blind and VI people in highly dynamic urban scenes. The system exploits two independent sources of information: ultrasonic sensors and the video camera embedded in a regular smartphone. The underlying methodology exploits computer vision and machine learning techniques and makes it possible to identify accurately both static and highly dynamic objects existent in a scene, regardless on their location, size or shape. In addition, the proposed system is able to acquire information about the environment, semantically interpret it and alert users about possible dangerous situations through acoustic feedback. To determine the performance of the proposed methodology we have performed an extensive objective and subjective experimental evaluation with the help of 21 VI subjects from two blind associations. The users pointed out that our prototype is highly helpful in increasing the mobility, while being friendly and easy to learn. View Full-Text
Keywords: wearable assistive device; obstacle detection; object recognition; acoustic feedback; ultrasonic network; computer vision techniques, machine learning algorithms wearable assistive device; obstacle detection; object recognition; acoustic feedback; ultrasonic network; computer vision techniques, machine learning algorithms
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Mocanu, B.; Tapu, R.; Zaharia, T. When Ultrasonic Sensors and Computer Vision Join Forces for Efficient Obstacle Detection and Recognition. Sensors 2016, 16, 1807.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top