A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems

The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning-based methods can spot face patch in an...

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Autores principales:	Hanchi Ren, Yi Hu, San Hlaing Myint, Kun Hou, Xiuyu Zhang, Min Zuo, Chi Zhang, Qingchuan Zhang, Haipeng Li
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/8157f66126fd4766998feb993804dce5
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spelling	oai:doaj.org-article:8157f66126fd4766998feb993804dce52021-11-08T02:36:18ZA Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems1530-867710.1155/2021/6711561https://doaj.org/article/8157f66126fd4766998feb993804dce52021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6711561https://doaj.org/toc/1530-8677The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning-based methods can spot face patch in an image effectively and accurately. Many people consider face tracking as face detection, but they are two different techniques. Face detection focuses on a single image, whose shortcoming is obvious, such as unstable and unsmooth face position when adopted on a sequence of continuous images; computing is expensive due to its heavy reliance on Convolutional Neural Networks (CNNs) and limited detection performance on the edge device. To overcome these defects, this paper proposes a novel face tracking strategy by combining CNN and optical flow, namely, C-OF, which achieves an extremely fast, stable, and long-term face tracking system. Two key things for commercial applications are the stability and smoothness of face positions in a sequence of image frames, which can provide more probability for face biological signal extraction, silent face antispoofing, and facial expression analysis in the fields of IoT-based MCS. Our method captures face patterns in every two consequent frames via optical flow to get rid of the unstable and unsmooth problems. Moreover, an innovative metric for measuring the stability and smoothness of face motion is designed and adopted in our experiments. The experimental results illustrate that our proposed C-OF outperforms both face detection and object tracking methods.Hanchi RenYi HuSan Hlaing MyintKun HouXiuyu ZhangMin ZuoChi ZhangQingchuan ZhangHaipeng LiHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Hanchi Ren Yi Hu San Hlaing Myint Kun Hou Xiuyu Zhang Min Zuo Chi Zhang Qingchuan Zhang Haipeng Li A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems
description	The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning-based methods can spot face patch in an image effectively and accurately. Many people consider face tracking as face detection, but they are two different techniques. Face detection focuses on a single image, whose shortcoming is obvious, such as unstable and unsmooth face position when adopted on a sequence of continuous images; computing is expensive due to its heavy reliance on Convolutional Neural Networks (CNNs) and limited detection performance on the edge device. To overcome these defects, this paper proposes a novel face tracking strategy by combining CNN and optical flow, namely, C-OF, which achieves an extremely fast, stable, and long-term face tracking system. Two key things for commercial applications are the stability and smoothness of face positions in a sequence of image frames, which can provide more probability for face biological signal extraction, silent face antispoofing, and facial expression analysis in the fields of IoT-based MCS. Our method captures face patterns in every two consequent frames via optical flow to get rid of the unstable and unsmooth problems. Moreover, an innovative metric for measuring the stability and smoothness of face motion is designed and adopted in our experiments. The experimental results illustrate that our proposed C-OF outperforms both face detection and object tracking methods.
format	article
author	Hanchi Ren Yi Hu San Hlaing Myint Kun Hou Xiuyu Zhang Min Zuo Chi Zhang Qingchuan Zhang Haipeng Li
author_facet	Hanchi Ren Yi Hu San Hlaing Myint Kun Hou Xiuyu Zhang Min Zuo Chi Zhang Qingchuan Zhang Haipeng Li
author_sort	Hanchi Ren
title	A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems
title_short	A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems
title_full	A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems
title_fullStr	A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems
title_full_unstemmed	A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems
title_sort	real-time and long-term face tracking method using convolutional neural network and optical flow in iot-based multimedia communication systems
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/8157f66126fd4766998feb993804dce5
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A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems

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