![](bg8.png)
Table 2: Comparison of run time for the two 3D vision modules (object detection and finger direction recognition in the
mobile device and web services.
Module Mobile Run Time (ms)
Web Services Run Time(ms)
Data Transmission Cloud Computation Total
Object Detection 73.01±4.45 267.05±98.19 201.36±26.65 468.41±104.40
Finger Direction
Recognition
56.84±4.51 344.11±126.45 48.69±10.37 392.81±130.17
would require us to expand on the existing user re-
quest mapping module and task scheduler to incorpo-
rate the necessary changes for future integration.
ACKNOWLEDGEMENTS
The work is supported by the US National Sci-
ence Foundation (#2131186, #2118006, #1827505,
#1737533, and #2048498), ODNI Intelligence Com-
munity Center for Academic Excellence (IC CAE) at
Rutgers (#HHM402-19-1-0003 and #HHM402-18-1-
0007) and the US Air Force Office for Scientific Re-
search (#FA9550-21-1-0082).
REFERENCES
Ahmad, N. S., Boon, N. L., and Goh, P. (2018). Multi-
sensor obstacle detection system via model-based
state-feedback control in smart cane design for the vi-
sually challenged. IEEE Access, 6:64182–64192.
Bai, J., Lian, S., Liu, Z., Wang, K., and Liu, D. (2018).
Virtual-blind-road following-based wearable naviga-
tion device for blind people. IEEE Transactions on
Consumer Electronics, 64(1):136–143.
Bouhamed, S. A., Kallel, I. K., and Masmoudi, D. S.
(2013). New electronic white cane for stair case de-
tection and recognition using ultrasonic sensor. Inter-
national Journal of Advanced Computer Science and
Applications, 4(6).
Chen, J. and Zhu, Z. (2022). Real-time 3d object detec-
tion and recognition using a smartphone [real-time 3d
object detection and recognition using a smartphone].
In Proceedings of the 2nd International Conference on
Image Processing and Vision Engineering-IMPROVE.
Ghani, F. A. and Zariman, A. (2019). Smart cane based
on iot. International Journal of Education, Science,
Technology, and Engineering, 2(1):12–18.
Glenn Jocher, Ayush Chaurasia, Alex Stoken, Jirka
Borovec, NanoCode012, Yonghye Kwon, TaoXie, Ji-
acong Fang, imyhxy, Kalen Michael; Lorna, Ab-
hiram V, Diego Montes, Jebastin Nadar, Laugh-
ing, tkianai, yxNONG, Piotr Skalski, Zhiqiang
Wang, Adam Hogan, Cristi Fati, Lorenzo Mammana,
AlexWang1900, Deep Patel, Ding Yiwei, Felix You,
Jan Hajek, Laurentiu Diaconu, Mai Thanh Minh
(2022). ultralytics/yolov5: v6.1 - TensorRT, Tensor-
Flow Edge TPU and OpenVINO Export and Infer-
ence. [Online]. Available from: https://doi.org/10.
5281/zenodo.3908559/.
Granquist, C., Sun, S. Y., Montezuma, S. R., Tran, T. M.,
Gage, R., and Legge, G. E. (2021). Evaluation and
comparison of artificial intelligence vision aids: Or-
cam myeye 1 and seeing ai. Journal of Visual Impair-
ment & Blindness, 115(4):277–285.
Grozdi
´
c,
–
D. T., Jovi
ˇ
ci
´
c, S. T., and Suboti
´
c, M. (2017). Whis-
pered speech recognition using deep denoising au-
toencoder. Engineering Applications of Artificial In-
telligence, 59:15–22.
Guerrero, J. C., Quezada-V, C., and Chacon-Troya, D.
(2018). Design and implementation of an intelligent
cane, with proximity sensors, gps localization and
gsm feedback. In 2018 IEEE Canadian Conference on
Electrical & Computer Engineering (CCECE), pages
1–4. IEEE.
Hakim, H. and Fadhil, A. (2019). Navigation system for
visually impaired people based on rgb-d camera and
ultrasonic sensor. In Proceedings of the International
Conference on Information and Communication Tech-
nology, pages 172–177.
Jothi, G., Azar, A. T., Qureshi, B., and Kamal, N. A. (2022).
ireader: An intelligent reader system for the visu-
ally impaired. In 2022 7th International Conference
on Data Science and Machine Learning Applications
(CDMA), pages 188–193. IEEE.
Jurafsky, D. and Martin, J. H. (2008). Speech and lan-
guage processing: An introduction to speech recog-
nition, computational linguistics and natural language
processing. Upper Saddle River, NJ: Prentice Hall.
Majeed, A. and Baadel, S. (2016). Facial recognition cane
for the visually impaired. In International Conference
on Global Security, Safety, and Sustainability, pages
394–405. Springer.
Rajendran, P. S., Krishnan, P., and Aravindhar, D. J. (2020).
Design and implementation of voice assisted smart
glasses for visually impaired people using google vi-
sion api. In 2020 4th International Conference on
Electronics, Communication and Aerospace Technol-
ogy (ICECA), pages 1221–1224. IEEE.
Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A.,
Sung, G., Chang, C.-L., and Grundmann, M. (2020).
Mediapipe hands: On-device real-time hand tracking.
arXiv preprint arXiv:2006.10214.
An Integrated Mobile Vision System for Enhancing the Interaction of Blind and Low Vision Users with Their Surroundings
187