VIPEye: Architecture and Prototype Implementation of Autonomous Mobility for Visually Impaired People

Comfortable movement of a visually impaired per-son in an unknown environment is non-trivial task due to com-plete or partial short-sightedness, absence of relevant information and unavailability of assistance from a non-impaired person. To fulfill the visual needs of an impaired person towards autonomous navigation, we utilize the concepts of graph mining and computer vision to produce a viable path guidance solution. We present an architectural perspective and a prototype implementation to determine safe & interesting path (SIP) from an arbitrary source to desired destination with intermediate way points, and guide visually impaired person through voice commands on that path. We also identify and highlight various challenging issues, that came up while developing a prototype solution, i.e. VIPEye -An Eye for Visually Impaired People, to this aforementioned problem, in terms of task’s difficulty and availability of required resources or information. Moreover, this study provides candidate research directions for researchers, developers, and practitioners in the development of autonomous mobility services for visually impaired people.

[1]  Titus Zaharia,et al.  Wearable assistive devices for visually impaired: A state of the art survey , 2020, Pattern Recognit. Lett..

[2]  Heng Tao Shen,et al.  Multi-source Skyline Query Processing in Road Networks , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[3]  Charles X. Ling,et al.  Pelee: A Real-Time Object Detection System on Mobile Devices , 2018, NeurIPS.

[4]  Md. Milon Islam,et al.  Developing Walking Assistants for Visually Impaired People: A Review , 2019, IEEE Sensors Journal.

[5]  Alvaro Araujo,et al.  Navigation Systems for the Blind and Visually Impaired: Past Work, Challenges, and Open Problems , 2019, Sensors.

[6]  Luis Payá,et al.  Using Global Appearance Descriptors to Solve Topological Visual SLAM , 2019, Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction.

[7]  Johannes Schöning,et al.  Grand challenges in accessible maps , 2019, Interactions.

[8]  P. Alcantarilla Vision based localization: from humanoid robots to visually impaired people , 2011 .

[9]  Rabia Jafri,et al.  Computer vision-based object recognition for the visually impaired in an indoors environment: a survey , 2013, The Visual Computer.

[10]  Hironobu Takagi,et al.  NavCog3: An Evaluation of a Smartphone-Based Blind Indoor Navigation Assistant with Semantic Features in a Large-Scale Environment , 2017, ASSETS.

[11]  Bo Yang,et al.  Learning 3D Scene Semantics and Structure from a Single Depth Image , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[12]  Gretchen A. Stevens,et al.  Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. , 2017, The Lancet. Global health.

[13]  Khaled M. Elleithy,et al.  Sensor-Based Assistive Devices for Visually-Impaired People: Current Status, Challenges, and Future Directions , 2017, Sensors.

[14]  Sei Ikeda,et al.  Visual SLAM algorithms: a survey from 2010 to 2016 , 2017, IPSJ Transactions on Computer Vision and Applications.

[15]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[16]  Eiji Kamioka,et al.  Distance measurement for self-driving cars using stereo camera , 2017 .

[17]  Omar Y. Al-Jarrah,et al.  A Survey on 3D Object Detection Methods for Autonomous Driving Applications , 2019, IEEE Transactions on Intelligent Transportation Systems.

[18]  Alireza Darvishy Assistive technologies : short overview and trends , 2018 .

[19]  Paramvir Bahl,et al.  Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices , 2015, SenSys.

[20]  Matti Pietikäinen,et al.  Deep Learning for Generic Object Detection: A Survey , 2018, International Journal of Computer Vision.

[21]  Liming Liu,et al.  The multi-criteria constrained shortest path problem , 2017 .