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Notice of retraction
Vol. 34, No. 8(3), S&M3042

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Sensors and Materials, Volume 35, Number 12(1) (2023)
Copyright(C) MYU K.K.
pp. 4117-4129
S&M3471 Research Paper of Special Issue
https://doi.org/10.18494/SAM4592
Published: December 15, 2023

Real-time Hand Movement Trajectory Tracking with Deep Learning [PDF]

Po-Tong Wang, Jia-Shing Sheu, and Chih-Fang Shen

(Received July 15, 2023; Accepted November 9, 2023)

Keywords: real-time hand tracking, deep learning, single-shot multibox detector (SSD), CAMShift, object detection, human–computer interaction (HCI)

In this study, we employed deep learning to develop a real-time hand trajectory tracking system. Our primary approach integrates the MobileNetv2 single-shot multibox detector, known for accuracy, with the versatile CAMShift algorithm. This synergy ensures robust hand detection across diverse scenarios. Through rigorous testing on webcam images and leveraging advanced feature extraction methods, such as contour discernment and skin hue differentiation, we report an 88.17% increase in detection accuracy over traditional models. Moreover, with a latency of merely 0.0343 s, our system demonstrates its prowess in immersive gaming and assistive devices for individuals with disabilities

Corresponding author: Po-Tong Wang


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Po-Tong Wang, Jia-Shing Sheu, and Chih-Fang Shen, Real-time Hand Movement Trajectory Tracking with Deep Learning, Sens. Mater., Vol. 35, No. 12, 2023, p. 4117-4129.



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