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  • Research Article
  • Open Access
  • Event Detection Using "Variable Module Graphs" for Home Care Applications

    EURASIP Journal on Advances in Signal Processing20072007:074243

    https://doi.org/10.1155/2007/74243

    • Received: 14 June 2006
    • Accepted: 16 January 2007
    • Published:

    Abstract

    Technology has reached new heights making sound and video capture devices ubiquitous and affordable. We propose a paradigm to exploit this technology for home care applications especially for surveillance and complex event detection. Complex vision tasks such as event detection in a surveillance video can be divided into subtasks such as human detection, tracking, recognition, and trajectory analysis. The video can be thought of as being composed of various features. These features can be roughly arranged in a hierarchy from low-level features to high-level features. Low-level features include edges and blobs, and high-level features include objects and events. Loosely, the low-level feature extraction is based on signal/image processing techniques, while the high-level feature extraction is based on machine learning techniques. Traditionally, vision systems extract features in a feed-forward manner on the hierarchy, that is, certain modules extract low-level features and other modules make use of these low-level features to extract high-level features. Along with others in the research community, we have worked on this design approach. In this paper, we elaborate on recently introduced V/M graph. We present our work on using this paradigm for developing applications for home care applications. Primary objective is surveillance of location for subject tracking as well as detecting irregular or anomalous behavior. This is done automatically with minimal human involvement, where the system has been trained to raise an alarm when anomalous behavior is detected.

    Keywords

    • Feature Extraction
    • Event Detection
    • Anomalous Behavior
    • Machine Learning Technique
    • Surveillance Video

    Authors’ Affiliations

    (1)
    Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801-2918, USA

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    Copyright

    © Amit Sethi et al. 2007

    This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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