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

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 11; pp. 1807 - 1 - 1807-23
• Main Authors: Mocanu, Bogdan, Tapu, Ruxandra, Zaharia, Titus
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.10.2016; MDPI
• Subjects: acoustic feedback; Acoustics; Adult; Aged; Blindness; Cell Phone - instrumentation; Computer vision; computer vision techniques, machine learning algorithms; Engineering Sciences; Humans; Image Processing, Computer-Assisted - instrumentation; Image Processing, Computer-Assisted - methods; Machine Learning; Middle Aged; object recognition; obstacle detection; Self-Help Devices; Signal and Image processing; ultrasonic network; Visually Impaired Persons; wearable assistive device; object recognition; acoustic feedback; wearable assistive device; obstacle detection; computer vision techniques, machine learning algorithms; ultrasonic network; Obstacle detection; Machine learning algorithms; Wearable assistive device; Ultrasonic network; Object recognition; Computer vision techniques; Acoustic feedback
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16111807

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.