Computer vision and deep learning-enabled UAVs: proposed use cases for visually impaired people in a smart city

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Nasralla, Moustafa, Rehman, Ikram ORCID: https://orcid.org/0000-0003-0115-9024, Sobnath, Drishty and Paiva, Sara (2019) Computer vision and deep learning-enabled UAVs: proposed use cases for visually impaired people in a smart city. In: Workshop on Deep-learning based computer vision for UAV in conjunction with CAIP 2019, 06 Sept 2019, Salerno, Italy.

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Official URL: https://doi.org/10.1007/978-3-030-29930-9_9

Abstract

Technological research and innovation have advanced at a rapid pace in recent years, and one group hoping to benefit from this, is visually impaired people (VIP). Technology may enable them to find new ways of travelling around smart cities, thus improving their quality of life (QoL). Currently, there are approximately 110 million VIP worldwide, and continuous research is crucial to find innovative solutions to their mobility problems. Recent advances such as the increase in Unmanned Aerial Vehicles (UAVs), smartphones and wearable devices, together with an ever-growing uptake of deep learning, computer vision, the Internet of Things (IoT), and virtual and augmented reality (VR)/(AR), have provided VIP with the hope of having an improved QoL. In particular, indoor and outdoor spaces could be improved with the use of such technologies to make them suitable for VIP. This paper examines use cases both indoors and outdoors and provides recommendations of how deep learning and computer vision-enabled UAVs could be employed in smart cities to improve the QoL for VIP in the coming years.

Item Type:	Conference or Workshop Item (Paper)
ISSN:	1865-0929
ISBN:	9783030299309
Identifier:	10.1007/978-3-030-29930-9_9
Page Range:	pp. 91-99
Identifier:	10.1007/978-3-030-29930-9_9
Additional Information:	The final authenticated version is available online at https://doi.org/10.1007/978-3-030-29930-9_9.
Keywords:	Deep Learning, Computer Vision, UAVs, Drone, Visually Impaired People (VIP), Smart City.
Subjects:	Computing
Depositing User:	Ikram Rehman
Date Deposited:	15 Jul 2019 07:29
Last Modified:	04 Nov 2024 12:49
URI:	https://repository.uwl.ac.uk/id/eprint/6245

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References

[1] C. Texeira, A. S. Toledo, A. da S. Amorim, S. T. Kofuji, and V. Rogério dos Santos, “Visual Impairment and Smart Cities: Perspectives on Mobility.,” JOJ Ophthalmol., vol. 3, 2017.
[2] Ali Jasim Ramadhan, “Wearable Smart System for Visually Impaired People,” Sensors , vol. 13, no. 3, p. 834, 2018.

[3] D. V. Gibson, G. Kozmetsky, and R. W. Smilor, The Technopolis phenomenon : smart cities, fast systems, global networks. Rowman & Littlefield Publishers, 1992.

[4] Knud Erik Skouby, Anri Kivimäki, Lotta Haukipuro, Per Lynggaard, and Iwona Windekilde, “Smart Cities and the Ageing Population”. In 32nd Meeting of WWRF, Marrakech, Morocco. Retrieved from:
https://pdfs.semanticscholar.org/d7a5/84f867996dbdf78a34697523c537dae218bc.pdf

[5] European Commission, “General Assembly of the European Innovation Partnership on Smart Cities and Communities (EIP-SCC) | European Commission,” 2018.

[6] N. Y. Philip and I. U. Rehman, “Towards 5G health for medical video streaming over small cells,” in IFMBE Proceedings, 2016.

[7] M. Avila, M. Funk, N. Henze, “Dronenavigator: Using drones for navigating visually impaired persons,”. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility (pp. 327-328), 2015.

[8] A. Voulodimos, N. Doulamis, A. Doulamis and E. Protopapadakis, "Deep learning for computer vision: A brief review." Computational intelligence and neuroscience, 2018.

[9] M. Liang, and X. Hu. "Recurrent convolutional neural network for object recognition." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3367-3375. 2015.

[10] G. E. Hinton and T. J. Sejnowski. "Learning and relearning in Boltzmann machines." Parallel distributed processing: Explorations in the microstructure of cognition, pp: 282-317, 1986.

[11] T. Baltrušaitis, C. Ahuja, and L. P. Morency. "Multimodal machine learning: A survey and taxonomy." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 2, pp: 423-443, 2018.

[12] A. Carrio, C. Sampedro, A. Rodriguez-Ramos, and P. Campoy. "A review of deep learning methods and applications for unmanned aerial vehicles." Journal of Sensors, 2017.

[13] C. Hung, Z. Xu, and S. Sukkarieh. "Feature learning based approach for weed classification using high resolution aerial images from a digital camera mounted on a UAV." Remote Sensing 6, no. 12, pp: 12037-12054, 2014.

[14] S. Jung, S. Hwang, H. Shin, and D. H. Shim. "Perception, guidance, and navigation for indoor autonomous drone racing using deep learning." IEEE Robotics and Automation Letters 3, no. 3, pp: 2539-2544, 2018.

[15] S. Cardin, D. Thalmann, and F. Vexo, “A wearable system for mobility improvement of visually impaired people,” Vis. Comput., 2007.

[16] L. D. Dunai, I. L. Lengua, I. Tortajada, and F. B. Simon, “Obstacle detectors for visually impaired people,” in 2014 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM, 2014.

[17] P. Gharani and H. A. Karimi, “Context-aware obstacle detection for navigation by visually impaired,” Image Vis. Comput., 2017.

[18] A. Jonnalagedda et al., “Enhancing the Safety of Visually Impaired Travelers in and around Transit Stations,” 2014.

[19] C. L. Lee, C. Y. Chen, P. C. Sung, and S. Y. Lu, “Assessment of a simple obstacle detection device for the visually impaired,” Appl. Ergon., 2014.

[20] M. Poggi and S. Mattoccia, “A wearable mobility aid for the visually impaired based on embedded 3D vision and deep learning,” in Proceedings - IEEE Symposium on Computers and Communications, 2016.

[21] M. Rahman, B. Poo, A. Amin, and H. Yan, “Support System Using Microsoft Kinect and Mobile Phone for Daily Activity of Visually Impaired,” in An Embedded SIP-VoIP Service in Enhanced Ethernet Passive Optical Network , 2015, pp. 425–440.

[22] M. Patricio, L. Haidee, L. Ciro, R. Telma, and F. Felipe, “Analysis and Proposed Improvements in the Support for the Visually Impaired in the Use of Public Transportation,” in SMART 2015 : The Fourth International Conference on Smart Systems, Devices and Technologies, 2015, pp. 978-1-61208-414–5.

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Computer vision and deep learning-enabled UAVs: proposed use cases for visually impaired people in a smart city

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