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  • Hybrid salient motion detection using temporal differencing and Kalman filter tracking with non-stationary camera

    Author(s)
    Le, Xuesong
    Gonzalez, Ruben
    Griffith University Author(s)
    Gonzalez, Ruben
    Le, Xuesong
    Year published
    2017
    Metadata
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    Abstract
    Uncertain motion of typical surveillance targets, e.g. slow moving or stopped, abrupt acceleration, and uniform motion makes a single salient motion detection algorithm unsuitable for accurate segmentation. It becomes even more challenging in case of the camera is non-stationary. In this paper, first, a simple local adaptive temporal differencing method is proposed to detect moving objects boundaries and partial interiors. To improve the accuracy of detection, a Bottom-up Variable Block Size block matching method is employed to identify the existence of possible moving object blocks and then an adaptive Kalman filter is ...
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    Uncertain motion of typical surveillance targets, e.g. slow moving or stopped, abrupt acceleration, and uniform motion makes a single salient motion detection algorithm unsuitable for accurate segmentation. It becomes even more challenging in case of the camera is non-stationary. In this paper, first, a simple local adaptive temporal differencing method is proposed to detect moving objects boundaries and partial interiors. To improve the accuracy of detection, a Bottom-up Variable Block Size block matching method is employed to identify the existence of possible moving object blocks and then an adaptive Kalman filter is applied to distinguish salient motions from other distracting motions. At last, the motion data from two algorithms are successfully fused to determine whether a region has been changed or not. Experimental results comparing the proposed and other competing methods are evaluated objectively and show that the proposed method achieves promising motion results for a variety of real environments.
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    Conference Title
    2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
    DOI
    https://doi.org/10.1109/ICIP.2017.8296902
    Subject
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/377059
    Collection
    • Conference outputs

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