Privacy-Aware Human-Detection and Tracking System Using Biological Signals

Toshihiro KITAJIMA; Edwardo Arata Y. MURAKAMI; Shunsuke YOSHIMOTO; Yoshihiro KURODA; Osamu OSHIRO

doi:10.1587/transcom.2018SEP0006

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Privacy-Aware Human-Detection and Tracking System Using Biological Signals

Toshihiro KITAJIMA, Edwardo Arata Y. MURAKAMI, Shunsuke YOSHIMOTO, Yoshihiro KURODA, Osamu OSHIRO

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The arrival of the era of the Internet of Things (IoT) has ensured the ubiquity of human-sensing technologies. Cameras have become inexpensive instruments for human sensing and have been increasingly used for this purpose. Because cameras produce large quantities of information, they are powerful tools for sensing; however, because camera images contain information allowing individuals to be personally identified, their use poses risks of personal privacy violations. In addition, because IoT-ready home appliances are connected to the Internet, camera-captured images of individual users may be unintentionally leaked. In developing our human-detection method [33], [34], we proposed techniques for detecting humans from unclear images in which individuals cannot be identified; however, a drawback of this method was its inability to detect moving humans. Thus, to enable tracking of humans even through the images are blurred to protect privacy, we introduce a particle-filter framework and propose a human-tracking method based on motion detection and heart-rate detection. We also show how the use of integral images [32] can accelerate the execution of our algorithms. In performance tests involving unclear images, the proposed method yields results superior to those obtained with the existing mean-shift method or with a face-detection method based on Haar-like features. We confirm the acceleration afforded by the use of integral images and show that the speed of our method is sufficient to enable real-time operation. Moreover, we demonstrate that the proposed method allows successful tracking even in cases where the posture of the individual changes, such as when the person lies down, a situation that arises in real-world usage environments. We discuss the reasons behind the superior behavior of our method in performance tests compared to those of other methods.

Publication: IEICE TRANSACTIONS on Communications Vol.E102-B No.4 pp.708-721

Publication Date: 2019/04/01

Publicized: 2018/10/15

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2018SEP0006

Type of Manuscript: Special Section PAPER (Special Section on Sensing, Wireless Networking, Data Collection, Analysis and Processing Technologies for Ambient Intelligence with Internet of Things)

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Authors

Toshihiro KITAJIMA
  Samsung R&D Institute Japan
Edwardo Arata Y. MURAKAMI
  Samsung R&D Institute Japan
Shunsuke YOSHIMOTO
  Osaka University
Yoshihiro KURODA
  Osaka University
Osamu OSHIRO
  Osaka University

Keyword

camera, heart rate, human detection, IoT, privacy

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Toshihiro KITAJIMA, Edwardo Arata Y. MURAKAMI, Shunsuke YOSHIMOTO, Yoshihiro KURODA, Osamu OSHIRO, "Privacy-Aware Human-Detection and Tracking System Using Biological Signals" in IEICE TRANSACTIONS on Communications, vol. E102-B, no. 4, pp. 708-721, April 2019, doi: 10.1587/transcom.2018SEP0006.
Abstract: The arrival of the era of the Internet of Things (IoT) has ensured the ubiquity of human-sensing technologies. Cameras have become inexpensive instruments for human sensing and have been increasingly used for this purpose. Because cameras produce large quantities of information, they are powerful tools for sensing; however, because camera images contain information allowing individuals to be personally identified, their use poses risks of personal privacy violations. In addition, because IoT-ready home appliances are connected to the Internet, camera-captured images of individual users may be unintentionally leaked. In developing our human-detection method [33], [34], we proposed techniques for detecting humans from unclear images in which individuals cannot be identified; however, a drawback of this method was its inability to detect moving humans. Thus, to enable tracking of humans even through the images are blurred to protect privacy, we introduce a particle-filter framework and propose a human-tracking method based on motion detection and heart-rate detection. We also show how the use of integral images [32] can accelerate the execution of our algorithms. In performance tests involving unclear images, the proposed method yields results superior to those obtained with the existing mean-shift method or with a face-detection method based on Haar-like features. We confirm the acceleration afforded by the use of integral images and show that the speed of our method is sufficient to enable real-time operation. Moreover, we demonstrate that the proposed method allows successful tracking even in cases where the posture of the individual changes, such as when the person lies down, a situation that arises in real-world usage environments. We discuss the reasons behind the superior behavior of our method in performance tests compared to those of other methods.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018SEP0006/_p

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@ARTICLE{e102-b_4_708,
author={Toshihiro KITAJIMA, Edwardo Arata Y. MURAKAMI, Shunsuke YOSHIMOTO, Yoshihiro KURODA, Osamu OSHIRO, },
journal={IEICE TRANSACTIONS on Communications},
title={Privacy-Aware Human-Detection and Tracking System Using Biological Signals},
year={2019},
volume={E102-B},
number={4},
pages={708-721},
abstract={The arrival of the era of the Internet of Things (IoT) has ensured the ubiquity of human-sensing technologies. Cameras have become inexpensive instruments for human sensing and have been increasingly used for this purpose. Because cameras produce large quantities of information, they are powerful tools for sensing; however, because camera images contain information allowing individuals to be personally identified, their use poses risks of personal privacy violations. In addition, because IoT-ready home appliances are connected to the Internet, camera-captured images of individual users may be unintentionally leaked. In developing our human-detection method [33], [34], we proposed techniques for detecting humans from unclear images in which individuals cannot be identified; however, a drawback of this method was its inability to detect moving humans. Thus, to enable tracking of humans even through the images are blurred to protect privacy, we introduce a particle-filter framework and propose a human-tracking method based on motion detection and heart-rate detection. We also show how the use of integral images [32] can accelerate the execution of our algorithms. In performance tests involving unclear images, the proposed method yields results superior to those obtained with the existing mean-shift method or with a face-detection method based on Haar-like features. We confirm the acceleration afforded by the use of integral images and show that the speed of our method is sufficient to enable real-time operation. Moreover, we demonstrate that the proposed method allows successful tracking even in cases where the posture of the individual changes, such as when the person lies down, a situation that arises in real-world usage environments. We discuss the reasons behind the superior behavior of our method in performance tests compared to those of other methods.},
keywords={},
doi={10.1587/transcom.2018SEP0006},
ISSN={1745-1345},
month={April},}

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TY - JOUR
TI - Privacy-Aware Human-Detection and Tracking System Using Biological Signals
T2 - IEICE TRANSACTIONS on Communications
SP - 708
EP - 721
AU - Toshihiro KITAJIMA
AU - Edwardo Arata Y. MURAKAMI
AU - Shunsuke YOSHIMOTO
AU - Yoshihiro KURODA
AU - Osamu OSHIRO
PY - 2019
DO - 10.1587/transcom.2018SEP0006
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2019
AB - The arrival of the era of the Internet of Things (IoT) has ensured the ubiquity of human-sensing technologies. Cameras have become inexpensive instruments for human sensing and have been increasingly used for this purpose. Because cameras produce large quantities of information, they are powerful tools for sensing; however, because camera images contain information allowing individuals to be personally identified, their use poses risks of personal privacy violations. In addition, because IoT-ready home appliances are connected to the Internet, camera-captured images of individual users may be unintentionally leaked. In developing our human-detection method [33], [34], we proposed techniques for detecting humans from unclear images in which individuals cannot be identified; however, a drawback of this method was its inability to detect moving humans. Thus, to enable tracking of humans even through the images are blurred to protect privacy, we introduce a particle-filter framework and propose a human-tracking method based on motion detection and heart-rate detection. We also show how the use of integral images [32] can accelerate the execution of our algorithms. In performance tests involving unclear images, the proposed method yields results superior to those obtained with the existing mean-shift method or with a face-detection method based on Haar-like features. We confirm the acceleration afforded by the use of integral images and show that the speed of our method is sufficient to enable real-time operation. Moreover, we demonstrate that the proposed method allows successful tracking even in cases where the posture of the individual changes, such as when the person lies down, a situation that arises in real-world usage environments. We discuss the reasons behind the superior behavior of our method in performance tests compared to those of other methods.
ER -