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Mobile User Identification by Camera-Based Motion Capture and Mobile Device Acceleration Sensors

Authors:

Takuya IndoAuthors Info & Claims

CHANTS '18: Proceedings of the 13th Workshop on Challenged Networks

Pages 25 - 31

https://doi.org/10.1145/3264844.3264846

Published: 01 October 2018 Publication History

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Abstract

Context-awareness using camera images is a promising technique for enabling ubiquitous computing and networking; however, it is still an open issue to identify mobile users, i.e., identifying an actual user with a mobile device from people in an area. This paper discusses a mobile user identification method mapping users in the camera images to mobile devices connected to an access point. The proposed scheme focuses on acceleration of a hand-held mobile device and that of a mobile user's hand, which synchronously vary when the mobile user utilizes the device. The scheme obtains the user's hand motion from cameras by motion capture, converts that data into acceleration of the user's hand, calculates the correlations between the value of the acceleration of the user's hands and devices, and solves a matching problem. Experimental results show that the proposed scheme identifies mobile users with 100% accuracy when users walk at 1 m/s or when users walk at 0.5 m/s and stop to use their mobile devices. The proposed scheme also identifies with greater than 94% accuracy even when the numbers of users and mobile devices are different.

References

[1]

Mykhaylo Andriluka, Roth Stefan, and Schiele Bernt . 2010. Monocular 3d pose estimation and tracking by detection Proc. IEEE CVPR. San Francisco, CA, USA, 623--630.

Google Scholar

[2]

L Ballan, M Bertini, AD Binbo, and W Nunziati . 2007. Soccer players identification based on visual local features Proc. IEEE ACM. Reno, NV, USA, 258--265.

Digital Library

Google Scholar

[3]

S Dang, J Ju, L Baker, A Gholamzadeh, and Y Li . 2014. Hybrid forecasting model of power demand based on three-stage synthesis and stochastically self-adapting mechanism. In ENERGYCON. 467--472.

Google Scholar

[4]

Dubois Didier and Henri Prade . 1992. Gradual inference rules in approximate reasoning. Information Science, Vol. 61, 1--2 (April . 1992), 103--122.

Digital Library

Google Scholar

[5]

T Hamatani, Y Sakaguchi, A Uchiyama, and T Higashino . 2016. Player identification by motion features in sport videos using wearable sensors Proc. IEEE ICarnegie Mellon University. Kaiserslautern, Kaiserslautern, Germany, 1--6.

Google Scholar

[6]

Deokwoo Jung, Thiago Teixeira, and Andreas Savvides . 2010. Towards cooperative localization of wearable sensors using accelerometers and cameras Proc. IEEE INFOCOM. San Diego, CA, USA, 1--9.

Digital Library

Google Scholar

[7]

Kinect . "https://developer.microsoft.com/ja-jp/windows/kinect". Kinect. (. "https://developer.microsoft.com/ja-jp/windows/kinect").

Google Scholar

[8]

Y Oguma, R Arai, T Nishio, K Yamamoto, and M Morikura . 2015. Proactive base station selection based on human blockage prediction using RGB-D cameras for mmWave communications. In IEEE Globecom. San Diego, CA, USA, 1--6.

Google Scholar

[9]

H Okamoto, T Nishio, M Morikura, and K Yamamoto . 2018. Recurrent neural network-based received signal strength estimation using depth images for mmWave communications. In Proc. IEEE CCNC. IEEE, Las Vegas, Nevada, USA, 1--2.

Crossref

Google Scholar

[10]

Raspberry Pi . "https://www.raspberrypi.org.". Raspberry Pi. (. "https://www.raspberrypi.org.").

Google Scholar

[11]

Michalis Vrigkas, Christophoros Nikou, and Ioannis A Kakadiaris . 2015. A review of human activity recognition methods. Frontiers in Robotics and AI Vol. 2 (Nov. . 2015), 28.

Google Scholar

[12]

C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland . 1997. Pfinder: Real-time tracking of the human body. IEEE Transactions on pattern analysis and machine intelligence, Vol. 19, 7 (July . 1997), 780--785.

Digital Library

Google Scholar

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Champa ARabbi MEishita FZibran M(2024)Are We Aware? An Empirical Study on the Privacy and Security Awareness of Smartphone SensorsSoftware Engineering and Management: Theory and Application10.1007/978-3-031-55174-1_10(139-158)Online publication date: 3-May-2024
https://doi.org/10.1007/978-3-031-55174-1_10
Champa ARabbi MEishita FZibran M(2023)Are We Aware? An Empirical Study on the Privacy and Security Awareness of Smartphone Sensors2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)10.1109/SERA57763.2023.10197713(287-294)Online publication date: 23-May-2023
https://doi.org/10.1109/SERA57763.2023.10197713
Yamanaka GNishio TMorikura MYamamoto KMaki YEitoku SIndo T(2019)Geo-Fencing in Wireless LANs with Camera for Location-Based Access Control2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC.2019.8651877(1-4)Online publication date: Jan-2019
https://doi.org/10.1109/CCNC.2019.8651877

Index Terms

Mobile User Identification by Camera-Based Motion Capture and Mobile Device Acceleration Sensors
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Matching

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Published In

CHANTS '18: Proceedings of the 13th Workshop on Challenged Networks

October 2018

77 pages

ISBN:9781450359269

DOI:10.1145/3264844

Program Chairs:
Marius Feldmann
Technische Universität Dresden, Germany
,
Mahbub Hassan
University of New South Wales, Australia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 01 October 2018

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MobiCom '18

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SIGMOBILE

MobiCom '18: The 24th Annual International Conference on Mobile Computing and Networking

October 29, 2018

New Delhi, India

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CHANTS '18 Paper Acceptance Rate 9 of 27 submissions, 33%;

Overall Acceptance Rate 61 of 159 submissions, 38%

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Cited By

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Champa ARabbi MEishita FZibran M(2024)Are We Aware? An Empirical Study on the Privacy and Security Awareness of Smartphone SensorsSoftware Engineering and Management: Theory and Application10.1007/978-3-031-55174-1_10(139-158)Online publication date: 3-May-2024
https://doi.org/10.1007/978-3-031-55174-1_10
Champa ARabbi MEishita FZibran M(2023)Are We Aware? An Empirical Study on the Privacy and Security Awareness of Smartphone Sensors2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)10.1109/SERA57763.2023.10197713(287-294)Online publication date: 23-May-2023
https://doi.org/10.1109/SERA57763.2023.10197713
Yamanaka GNishio TMorikura MYamamoto KMaki YEitoku SIndo T(2019)Geo-Fencing in Wireless LANs with Camera for Location-Based Access Control2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC.2019.8651877(1-4)Online publication date: Jan-2019
https://doi.org/10.1109/CCNC.2019.8651877

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Management of security policies for mobile devices

Mobile device security

Enhancing the human-computer interaction on camera-equipped mobile phones

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