An extensive collection of algorithms have been proposed over the years to identify the foreground from a video scene, but none of them considers the classification history of previous frames for discovering moving objects. All the existing algorithms focus on a single background model to cope with all types of challenging and complex video environments. In this paper, a real-time pixel level classification method is proposed that uses its previous output history to update its parameters. The model has three components designed to handle various types of challenging background environments. For each pixel, three sub-parts, namely the Adaptive Background Model, the Neighborhood Background Model, and the Change Detection Background Model are constructed to detect various types of complex background changes. A pixel level model updating method uses the previous foreground/background binary classification results to periodically refresh all the three background models. This update method helps to recognize the foreground accurately by adjusting the algorithmic parameters to efficiently detect complex background changes. This novel method shows a significant improvement in performance for a variety of complex video scenes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Foreground Segmentation Using Adaptive 3 Phase Background Model


    Contributors:


    Publication date :

    2020-06-01


    Size :

    1655788 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    An Adaptive Background Modeling Method for Foreground Segmentation

    Zhong, Zuofeng / Zhang, Bob / Lu, Guangming et al. | IEEE | 2017




    Background Foreground Segmentation for SLAM

    Corcoran, P. / Winstanley, A. / Mooney, P. et al. | IEEE | 2011


    Two video analysis applications using foreground/background segmentation

    Zivkovic, Z. / Petkovic, M. / van Mierlo, R. et al. | British Library Conference Proceedings | 2003