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    Minimum Delay Moving Object Detection

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    Name:
    Lao_Minimum_Delay_Moving_CVPR_2017_paper.pdf
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    17.67Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
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    Type
    Conference Paper
    Authors
    Lao, Dong cc
    Sundaramoorthi, Ganesh cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    Date
    2017-11-09
    Online Publication Date
    2017-11-09
    Print Publication Date
    2017-07
    Permanent link to this record
    http://hdl.handle.net/10754/626820
    
    Metadata
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    Abstract
    We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames, more than two frames are needed to reliably detect the object. Our method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.
    Citation
    Lao D, Sundaramoorthi G (2017) Minimum Delay Moving Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/cvpr.2017.511.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    Conference/Event name
    30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    DOI
    10.1109/cvpr.2017.511
    Additional Links
    http://ieeexplore.ieee.org/document/8099994/
    ae974a485f413a2113503eed53cd6c53
    10.1109/cvpr.2017.511
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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