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Accurate Positioning via Cross-Modality Training

Published: 01 November 2015 Publication History
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  • Abstract

    In this paper we propose a novel algorithm for tracking people in highly dynamic industrial settings, such as construction sites. We observed both short term and long term changes in the environment; people were allowed to walk in different parts of the site on different days, the field of view of fixed cameras changed over time with the addition of walls, whereas radio and magnetic maps proved unstable with the movement of large structures. To make things worse, the uniforms and helmets that people wear for safety make them very hard to distinguish visually, necessitating the use of additional sensor modalities. In order to address these challenges, we designed a positioning system that uses both anonymous and id-linked sensor measurements and explores the use of cross-modality training to deal with environment dynamics. The system is evaluated in a real construction site and is shown to outperform state of the art multi-target tracking algorithms designed to operate in relatively stable environments.

    References

    [1]
    S. Blackman. Multiple hypothesis tracking for multiple target tracking. Aerospace and Electronic Systems Magazine, IEEE, 19(1):5--18, Jan 2004.
    [2]
    S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems. Artech House radar library. Artech House, 1999.
    [3]
    F. Bourgeois and J.-C. Lassalle. An extension of the munkres algorithm for the assignment problem to rectangular matrices. Commun. ACM, 14(12):802--804, Dec. 1971.
    [4]
    T. Bouwmans, F. El Baf, and B. Vachon. Background modeling using mixture of gaussians for foreground detection-a survey. Recent Patents on Computer Science, 1(3):219--237, 2008.
    [5]
    A. Doucet, N. De Freitas, and N. Gordon. Sequential monte carlo methods in practice. Springer-Verlag, 2001.
    [6]
    A. Doucet, N. d. Freitas, K. P. Murphy, and S. J. Russell. Rao-blackwellised particle filtering for dynamic bayesian networks. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, UAI '00, pages 176--183, San Francisco, CA, USA, 2000. Morgan Kaufmann Publishers Inc.
    [7]
    F. Fleuret, J. Berclaz, R. Lengagne, and P. Fua. Multicamera people tracking with a probabilistic occupancy map. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(2):267--282, Feb 2008.
    [8]
    T. E. Fortmann, Y. Bar-Shalom, and M. Scheffe. Sonar tracking of multiple targets using joint probabilistic data association. IEEE Journal of Oceanic Engineering, 8(3):173--184, 1983.
    [9]
    C. Hue, J.-P. Le Cadre, and P. Perez. Tracking multiple objects with particle filtering. Aerospace and Electronic Systems, IEEE Transactions on, 38(3):791--812, Jul 2002.
    [10]
    S. Julier and J. Uhlmann. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3):401--422, Mar 2004.
    [11]
    D. Jung, T. Teixeira, and A. Savvides. Towards cooperative localization of wearable sensors using accelerometers and cameras. In INFOCOM, 2010 Proceedings IEEE, pages 1--9, March 2010.
    [12]
    H. W. Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1--2):83--97, 1955.
    [13]
    D. Lee, I. Hwang, and S. Oh. Optimus:online persistent tracking and identification of many users for smart spaces. Machine Vision and Applications, 25(4):901--917, 2014.
    [14]
    D. Lymberopoulos, J. Liu, X. Yang, R. R. Choudhury, S. Sen, and V. Handzinski. Microsoft indoor localization competition: Experiences and lessons learned. SIGMOBILE Mobile Computation and Communication Review (MC2R), October 2014.
    [15]
    R. Mandeljc, S. Kovacic, M. Kristan, and J. Pers. Tracking by identification using computer vision and radio. Sensors, 13(1):241--273, 2012.
    [16]
    R. Mandeljc, J. Pers, M. Kristan, and S. Kovacic. Fusion of non-visual modalities into the probabilistic occupancy map framework for person localization. In Distributed Smart Cameras (ICDSC), 2011 Fifth ACM/IEEE International Conference on, pages 1--6, Aug 2011.
    [17]
    R. Mautz. Indoor positioning technologies. Habilitation thesis, ETH Zurich, 2012.
    [18]
    J. Ning, L. Zhang, D. Zhang, and C. Wu. Robust mean-shift tracking with corrected background-weighted histogram. Computer Vision, IET, 6(1):62--69, January 2012.
    [19]
    S. Papaioannou, H. Wen, A. Markham, and N. Trigoni. Fusion of radio and camera sensor data for accurate indoor positioning. In Mobile Ad Hoc and Sensor Systems (MASS), 2014 IEEE 11th International Conference on, pages 109--117, Oct 2014.
    [20]
    D. Reid. An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control, 24(6):843--854, 1979.
    [21]
    V. Renaudin, M. Susi, and G. Lachapelle. Step length estimation using handheld inertial sensors. Sensors, 12(7):8507--8525, 2012.
    [22]
    S. Särkkä, A. Vehtari, and J. Lampinen. Rao-blackwellized monte carlo data association for multiple target tracking. In Proceedings of the seventh international conference on information fusion, volume 1, pages 583--590. I, 2004.
    [23]
    S. Särkkä, A. Vehtari, and J. Lampinen. Rao-blackwellized particle filter for multiple target tracking. Information Fusion, 8(1):2 -- 15, 2007. Special Issue on the Seventh International Conference on Information Fusion-PartII Seventh International Conference on Information Fusion.
    [24]
    S. Seidel and T. Rappaport. 914 mhz path loss prediction models for indoor wireless communications in multifloored buildings. Antennas and Propagation, IEEE Transactions on, 40(2):207--217, 1992.
    [25]
    M. Shah, J. Deng, and B. Woodford. Video background modeling: recent approaches, issues and our proposed techniques. Machine Vision and Applications, 25(5):1105--1119, 2014.
    [26]
    A. Sobral and A. Vacavant. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Computer Vision and Image Understanding, 122(0):4--21, 2014.
    [27]
    C. Stauffer and W. Grimson. Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., volume 2, pages --252 Vol. 2, 1999.
    [28]
    T. Teixeira, D. Jung, G. Dublon, and A. Savvides. Identifying people in camera networks using wearable accelerometers. In Proceedings of the 2Nd International Conference on PErvasive Technologies Related to Assistive Environments, PETRA '09, pages 20:1--20:8, New York, NY, USA, 2009. ACM.
    [29]
    B. Zhang, J. Teng, J. Zhu, X. Li, D. Xuan, and Y. F. Zheng. Ev-loc: Integrating electronic and visual signals for accurate localization. In Proceedings of the Thirteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc '12, pages 25--34, New York, NY, USA, 2012. ACM.

    Cited By

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    • (2024)Tracking people across ultra populated indoor spaces by matching unreliable Wi-Fi signals with disconnected video feedsPervasive and Mobile Computing10.1016/j.pmcj.2023.10186097(101860)Online publication date: Jan-2024
    • (2023)Layout Sequence Prediction From Noisy Mobile ModalityProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611936(3965-3974)Online publication date: 26-Oct-2023
    • (2022)RFCamProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35345886:2(1-29)Online publication date: 7-Jul-2022
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        cover image ACM Conferences
        SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
        November 2015
        526 pages
        ISBN:9781450336314
        DOI:10.1145/2809695
        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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        Publication History

        Published: 01 November 2015

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        Author Tags

        1. tracking
        2. wireless sensor networks

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        SenSys '15 Paper Acceptance Rate 27 of 132 submissions, 20%;
        Overall Acceptance Rate 174 of 867 submissions, 20%

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        View all
        • (2024)Tracking people across ultra populated indoor spaces by matching unreliable Wi-Fi signals with disconnected video feedsPervasive and Mobile Computing10.1016/j.pmcj.2023.10186097(101860)Online publication date: Jan-2024
        • (2023)Layout Sequence Prediction From Noisy Mobile ModalityProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611936(3965-3974)Online publication date: 26-Oct-2023
        • (2022)RFCamProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35345886:2(1-29)Online publication date: 7-Jul-2022
        • (2022)Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene LabelingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.3196364(1-14)Online publication date: 2022
        • (2022)Multi-Modal Context Propagation for Person Re-Identification With Wireless PositioningIEEE Transactions on Multimedia10.1109/TMM.2021.309257924(3060-3073)Online publication date: 2022
        • (2022)iMag+: An Accurate and Rapidly Deployable Inertial Magneto-Inductive SLAM SystemIEEE Transactions on Mobile Computing10.1109/TMC.2021.306281321:10(3644-3655)Online publication date: 1-Oct-2022
        • (2022)Why and What?Wireless Localization Techniques10.1007/978-3-031-21178-2_1(1-10)Online publication date: 9-Nov-2022
        • (2021)UniLoc: A Unified Mobile Localization Framework Exploiting Scheme DiversityIEEE Transactions on Mobile Computing10.1109/TMC.2020.297985720:7(2505-2517)Online publication date: 1-Jul-2021
        • (2020)Vision Meets Wireless Positioning: Effective Person Re-identification with Recurrent Context PropagationProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413984(1103-1111)Online publication date: 12-Oct-2020
        • (2020)Jointly-Optimized Searching and Tracking with Random Finite SetsIEEE Transactions on Mobile Computing10.1109/TMC.2019.292213319:10(2374-2391)Online publication date: 1-Oct-2020
        • Show More Cited By

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