In this paper we propose an effective and wearable mobility aid for people suffering of visual impairments purely based on 3D computer vision and machine learning techniques. By wearing our device the users can perceive, guided by audio messages and tactile feedback, crucial information concerned with the surrounding environment and hence avoid obstacles along the path. Our proposal can work in synergy with the white cane and allows for very effective and real-time obstacle detection on an embedded computer, by processing the point-cloud provided by a custom RGBD sensor, based on passive stereo vision. Moreover, our system, leveraging on deep-learning techniques, enables to semantically categorize the detected obstacles in order to increase the awareness of the explored environment. It can optionally work in synergy with a smartphone, wirelessly connected to the the proposed mobility aid, exploiting its audio capability and standard GPS-based navigation tools such as Google Maps. The overall system can operate in real-time for hours using a small battery, making it suitable for everyday life. Experimental results confirmed that our proposal has excellent obstacle detection performance and has a promising semantic categorization capability.
A wearable mobility aid for the visually Impaired based on embedded 3D vision and deep learning / Poggi, Matteo; Mattoccia, Stefano. - ELETTRONICO. - (2016), pp. 208-213. (Intervento presentato al convegno First IEEE Workshop on ICT Solutions for eHealth (IEEE ICTS4eHealth 2016) in conjunction with the Twenty-First IEEE Symposium on Computers and Communications tenutosi a Messina nel June 27-30, 2016) [10.1109/ISCC.2016.7543741].
A wearable mobility aid for the visually Impaired based on embedded 3D vision and deep learning
POGGI, MATTEO;MATTOCCIA, STEFANO
2016
Abstract
In this paper we propose an effective and wearable mobility aid for people suffering of visual impairments purely based on 3D computer vision and machine learning techniques. By wearing our device the users can perceive, guided by audio messages and tactile feedback, crucial information concerned with the surrounding environment and hence avoid obstacles along the path. Our proposal can work in synergy with the white cane and allows for very effective and real-time obstacle detection on an embedded computer, by processing the point-cloud provided by a custom RGBD sensor, based on passive stereo vision. Moreover, our system, leveraging on deep-learning techniques, enables to semantically categorize the detected obstacles in order to increase the awareness of the explored environment. It can optionally work in synergy with a smartphone, wirelessly connected to the the proposed mobility aid, exploiting its audio capability and standard GPS-based navigation tools such as Google Maps. The overall system can operate in real-time for hours using a small battery, making it suitable for everyday life. Experimental results confirmed that our proposal has excellent obstacle detection performance and has a promising semantic categorization capability.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
