A spatially distributed model for foreground segmentation

DICKINSON, Patrick, HUNTER, Andrew and APPIAH, Kofi (2009). A spatially distributed model for foreground segmentation. Image and Vision Computing, 27 (9), 1326-1335.

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Official URL: https://www.sciencedirect.com/science/article/pii/...
Link to published version:: https://doi.org/10.1016/j.imavis.2008.12.001
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    Foreground segmentation is a fundamental first processing stage for vision systems which monitor real-world activity. In this paper, we consider the problem of achieving robust segmentation in scenes where the appearance of the background varies unpredictably over time. Variations may be caused by processes such as moving water, or foliage moved by wind, and typically degrade the performance of standard per-pixel background models. Our proposed approach addresses this problem by modeling homogeneous regions of scene pixels as an adaptive mixture of Gaussians in color and space. Model components are used to represent both the scene background and moving foreground objects. Newly observed pixel values are probabilistically classified, such that the spatial variance of the model components supports correct classification even when the background appearance is significantly distorted. We evaluate our method over several challenging video sequences, and compare our results with both per-pixel and Markov Random Field based models. Our results show the effectiveness of our approach in reducing incorrect classifications.

    Item Type: Article
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
    Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
    Identification Number: https://doi.org/10.1016/j.imavis.2008.12.001
    Page Range: 1326-1335
    Depositing User: Kofi Appiah
    Date Deposited: 23 Aug 2018 09:08
    Last Modified: 23 Aug 2018 09:08
    URI: http://shura.shu.ac.uk/id/eprint/22197

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