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    Detection of dynamic background due to swaying movements from motion features

    231837_231837.pdf (688.8Kb)
    Access Status
    Open access
    Authors
    Pham, DucSon
    Arandjelovic, O.
    Venkatesh, S.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Pham, D. and Arandjelovic, O. and Venkatesh, S. 2015. Detection of dynamic background due to swaying movements from motion features. IEEE Transactions on Image Processing. 24 (1): pp. 332-344.
    Source Title
    IEEE Transactions on Image Processing
    DOI
    10.1109/TIP.2014.2378034
    ISSN
    1057-7149
    School
    Department of Computing
    Remarks

    Copyright © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/8609
    Collection
    • Curtin Research Publications
    Abstract

    Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: (1) that of swaying tree branches and (2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras.The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art - n the dynamic background literature.

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