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Fusion of wifi and visual signals for person tracking

Published: 08 December 2016 Publication History

Abstract

Person tracking is crucial in any automatic person surveillance systems. In this problem, person localization and re-identification (Re-ID) are both simultaneously processed to show separated trajectories for each individual. In this paper, we propose to use mixture of WiFi and camera systems for person tracking in indoor surveillance regions covered by WiFi signals and disjointed camera FOVs (Field of View). A fusion method is proposed to combine the position observations achieved from each single system of WiFi or camera. The combination is done based on an optimal assignment between the position observations and predicted states from camera and WiFi systems. The correction step of Kalman filter is then applied for each tracker to give out state estimations of locations. The fusion method allows tracking by identification in non-overlapping cameras, with clear identity information taken from WiFi adapter. The experiments on a multi-model dataset show outperforming tracking results of the proposed fusion method in comparison with vision-based only method.

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Cited By

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  • (2022)Introduction to Healthcare-Oriented Monitoring of PersonsNon-invasive Monitoring of Elderly Persons10.1007/978-3-030-96009-4_1(1-39)Online publication date: 16-Apr-2022
  • (2020)Survey on WiFi‐based indoor positioning techniquesIET Communications10.1049/iet-com.2019.105914:9(1372-1383)Online publication date: Jun-2020
  • (2020)Comparison of sixteen methods for fusion of data from impulse-radar sensors and depth sensors applied for monitoring of elderly personsMeasurement10.1016/j.measurement.2019.107455154(107455)Online publication date: Mar-2020
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  1. Fusion of wifi and visual signals for person tracking

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    cover image ACM Other conferences
    SoICT '16: Proceedings of the 7th Symposium on Information and Communication Technology
    December 2016
    442 pages
    ISBN:9781450348157
    DOI:10.1145/3011077
    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: 08 December 2016

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    Author Tags

    1. Kalman filter
    2. camera
    3. fusion method
    4. optimal algorithm
    5. person tracking
    6. wifi

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    • Research-article

    Funding Sources

    • Vietnam National Foundation for Science and Technology Development (NAFOSTED)

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    SoICT '16

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    SoICT '16 Paper Acceptance Rate 58 of 132 submissions, 44%;
    Overall Acceptance Rate 147 of 318 submissions, 46%

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    Cited By

    View all
    • (2022)Introduction to Healthcare-Oriented Monitoring of PersonsNon-invasive Monitoring of Elderly Persons10.1007/978-3-030-96009-4_1(1-39)Online publication date: 16-Apr-2022
    • (2020)Survey on WiFi‐based indoor positioning techniquesIET Communications10.1049/iet-com.2019.105914:9(1372-1383)Online publication date: Jun-2020
    • (2020)Comparison of sixteen methods for fusion of data from impulse-radar sensors and depth sensors applied for monitoring of elderly personsMeasurement10.1016/j.measurement.2019.107455154(107455)Online publication date: Mar-2020
    • (2017)Fusion of measurement data from impulse-radar sensors and depth sensors when applied for patients monitoring2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)10.1109/CIVEMSA.2017.7995327(205-210)Online publication date: Jun-2017

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