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    Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring

    Zhou, Zhongna
    Chen, Xi
    Chung, Yu-Chia, 1979-
    He, Zhihai, 1973-
    Han, Tony X.
    Keller, James M.
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    [PDF] ActivityAnalysisHumanActivityMonitoring.pdf (1.467Mb)
    Date
    2008-11
    Format
    Article
    Metadata
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    Abstract
    In this work, we study how continuous video monitoring and intelligent video processing can be used in eldercare to assist the independent living of elders and to improve the efficiency of eldercare practice. More specifically, we develop an automated activity analysis and summarization for eldercare video monitoring. At the object level, we construct an advanced silhouette extraction, human detection and tracking algorithm for indoor environments. At the feature level, we develop an adaptive learning method to estimate the physical location and moving speed of a person from a single camera view without calibration. At the action level, we explore hierarchical decision tree and dimension reduction methods for human action recognition. We extract important ADL (activities of daily living) statistics for automated functional assessment. To test and evaluate the proposed algorithms and methods, we deploy the camera system in a real living environment for about a month and have collected more than 200 hours (in excess of 600 G bytes) of activity monitoring videos. Our extensive tests over these massive video datasets demonstrate that the proposed automated activity analysis system is very efficient.
    URI
    http://hdl.handle.net/10355/9260
    Part of
    Computer and Electrical Engineering publications (MU)
    Citation
    IEEE Transactions on Circuits and Systems for Video Technology, VOL. 18, NO. 11, November 2008.
    Rights
    OpenAccess.
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
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    • Electrical Engineering and Computer Science publications (MU)

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