5 March 2018 SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos
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Abstract
Change detection with background subtraction process remains to be an unresolved issue and attracts research interest due to challenges encountered on static and dynamic scenes. The key challenge is about how to update dynamically changing backgrounds from frames with an adaptive and self-regulated feedback mechanism. In order to achieve this, we present an effective change detection algorithm for pixelwise changes. A sliding window approach combined with dynamic control of update parameters is introduced for updating background frames, which we called sliding window-based change detection. Comprehensive experiments on related test videos show that the integrated algorithm yields good objective and subjective performance by overcoming illumination variations, camera jitters, and intermittent object motions. It is argued that the obtained method makes a fair alternative in most types of foreground extraction scenarios; unlike case-specific methods, which normally fail for their nonconsidered scenarios.
© 2018 SPIE and IS&T
Sahin Isik, Kemal Özkan, Serkan Günal, Ömer Nezih Gerek, "SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos," Journal of Electronic Imaging 27(2), 023002 (5 March 2018). https://doi.org/10.1117/1.JEI.27.2.023002 . Submission: Received: 25 September 2017; Accepted: 7 February 2018
Received: 25 September 2017; Accepted: 7 February 2018; Published: 5 March 2018
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