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Depth Camera and Electromagnetic Field Localization System For IoT Application: High level, lightweight data fusion

Published: 29 June 2021 Publication History

Abstract

This article demonstrates person localization using a hybrid system consisting of an electromagnetic positioning system and a depth camera to authorize access control. The ultimate aim of this system is to distinguish moving people in a defined area by tracking the RF device and the people. It focuses on the application and incorporation of the received data from these two systems. Both systems send data simultaneously which is stored in a Docker container for further analysis. The data is processed in real-time to track the movement of the targets. The centralized database monitoring grants secure access to the information. The motive for using this hybrid system lies in the ever-growing need for accurate position determination for indoor and complex environments. Track and tracing are especially important in access-control applications. The system has a great impact on real-life access-control applications in malls, shops, train stations, and generally everyplace where the access control requires monitoring. The non-blocking feature plus the accuracy can provide ease of use for the users. Moreover, employing a low-frequency tag system does not suffer from the multipath effect and non-line of sight problems that are inevitable for indoor applications. By extending the number of users for a larger area, this system can replace traditional security gates with a pleasant look and comfortable application.

References

[1]
J. Minar, K. Riha, and H. Tong, “Intruder detection for automated access control systems with Kinect device,” in 2013 36th International Conference on Telecommunications and Signal Processing (TSP), 2013, pp. 826–829.
[2]
S. Anchal, B. Mukhopadhyay, and S. Kar, “Person Identification and Imposter Detection using Footstep generated Seismic Signals,” IEEE Trans. Instrum. Meas., p. 1, 2020.
[3]
N. Podevijn, “TDoA-Based Outdoor Positioning with Tracking Algorithm in a Public LoRa Network,” Wirel. Commun. Mob. Comput., vol. 2018, 2018.
[4]
D. Dardari, P. Closas, and P. M. Djuric, “Indoor tracking: Theory, methods, and technologies,” IEEE Trans. Veh. Technol., vol. 64, no. 4, pp. 1263–1278, 2015.
[5]
D. Dardari, M. Luise, and E. Falletti, Satellite and terrestrial radio positioning techniques: a signal processing perspective. Academic Press, 2012.
[6]
H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 37, no. 6, pp. 1067–1080, 2007.
[7]
R. Mautz and S. Tilch, “Survey of optical indoor positioning systems,” 2011 Int. Conf. Indoor Position. Indoor Navig. IPIN 2011, pp. 1–7, 2011.
[8]
P. Hansen, “Magnetic Position and Orientation Measurement System,” US Pat. 4,622,644, no. 5, pp. 709–718, 1986, [Online]. Available: http://www.google.com/patents?hl=en&lr=&vid=USPAT4622644&id=eT81AAAAEBAJ&oi=fnd&dq=Magnetic+Position+and+Orientation+Measurement+System&printsec=abstract.
[9]
E. Paperno, I. Sasada, and E. Leonovich, “Tracking,” vol. 37, no. 4, pp. 1938–1940, 2001.
[10]
W. Storms, J. Shockley, and J. Raquet, “Magnetic field navigation in an indoor environment,” 2010 Ubiquitous Position. Indoor Navig. Locat. Based Serv. UPINLBS 2010, pp. 1–10, 2010.
[11]
L. Del Pizzo, P. Foggia, A. Greco, G. Percannella, and M. Vento, “A versatile and effective method for counting people on either RGB or depth overhead cameras,” in 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2015, pp. 1–6.
[12]
R. Iguernaissi, D. Merad, and P. Drap, “People Counting based on Kinect Depth Data.,” in ICPRAM, 2018, pp. 364–370.
[13]
D. Fuentes-Jimenez, “DPDnet: A robust people detector using deep learning with an overhead depth camera,” Expert Syst. Appl., vol. 146, p. 113168, 2020.
[14]
B. Antić, D. Letić, D. Ćulibrk, and V. Crnojević, “K-means based segmentation for real-time zenithal people counting,” in 2009 16th IEEE International Conference on Image Processing (ICIP), 2009, pp. 2565–2568.
[15]
C. A. Luna, C. Losada-Gutierrez, D. Fuentes-Jimenez, A. Fernandez-Rincon, M. Mazo, and J. Macias-Guarasa, “Robust people detection using depth information from an overhead Time-of-Flight camera,” Expert Syst. Appl., vol. 71, pp. 240–256, 2017.
[16]
B. Li, T. Gallagher, A. G. Dempster, and C. Rizos, “How feasible is the use of magnetic field alone for indoor positioning?,” in 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2012, pp. 1–9.
[17]
J. Chung, M. Donahoe, C. Schmandt, I.-J. Kim, P. Razavai, and M. Wiseman, “Indoor location sensing using geo-magnetism,” in Proceedings of the 9th international conference on Mobile systems, applications, and services, 2011, pp. 141–154.
[18]
C. E. Galván-Tejada, J. P. García-Vázquez, and R. F. Brena, “Magnetic field feature extraction and selection for indoor location estimation,” Sensors, vol. 14, no. 6, pp. 11001–11015, 2014.
[19]
R. Montoliu, J. Torres-Sospedra, and O. Belmonte, “Magnetic field based indoor positioning using the Bag of Words paradigm,” 2016 Int. Conf. Indoor Position. Indoor Navig. IPIN 2016, no. October, pp. 1–7, 2016.
[20]
“AmfiTrack EMF Picture.” https://www.ineltek.com/en/premo-3d-electromagnetic-motion-tracking-technology/.
[21]
“RealSense,” [Online]. Available: https://www.intelrealsense.com/depth-camera-d435i/.

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  • (2023)ICE-NeRF: Interactive Color Editing of NeRFs via Decomposition-Aware Weight Optimization2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00323(3468-3478)Online publication date: 1-Oct-2023
  • (2023)PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01982(20691-20700)Online publication date: Jun-2023
  • (2021)Bluetooth Low Energy Direction Finding Principle2021 24th International Conference on Electrical Machines and Systems (ICEMS)10.23919/ICEMS52562.2021.9634353(830-834)Online publication date: 31-Oct-2021

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    cover image ACM Other conferences
    ASSE '21: 2021 2nd Asia Service Sciences and Software Engineering Conference
    February 2021
    143 pages
    ISBN:9781450389082
    DOI:10.1145/3456126
    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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    New York, NY, United States

    Publication History

    Published: 29 June 2021

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    Author Tags

    1. Image processing
    2. Internet of Things
    3. KNN Algorithm
    4. Message Quest Telemetry Transport (MQTT)
    5. Radio Frequency positioning systems
    6. signal processing

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    View all
    • (2023)ICE-NeRF: Interactive Color Editing of NeRFs via Decomposition-Aware Weight Optimization2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00323(3468-3478)Online publication date: 1-Oct-2023
    • (2023)PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01982(20691-20700)Online publication date: Jun-2023
    • (2021)Bluetooth Low Energy Direction Finding Principle2021 24th International Conference on Electrical Machines and Systems (ICEMS)10.23919/ICEMS52562.2021.9634353(830-834)Online publication date: 31-Oct-2021

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