Universal Background Subtraction Using Word Consensus Models

@article{StCharles2016UniversalBS,
  title={Universal Background Subtraction Using Word Consensus Models},
  author={Pierre-Luc St-Charles and Guillaume-Alexandre Bilodeau and Robert Bergevin},
  journal={IEEE Transactions on Image Processing},
  year={2016},
  volume={25},
  pages={4768-4781}
}
Background subtraction is often used as the first step in video analysis and smart surveillance applications. However, the issue of inconsistent performance across different scenarios due to a lack of flexibility remains a serious concern. To address this, we propose a novel non-parametric, pixel-level background modeling approach based on word dictionaries that draws from traditional codebooks and sample consensus approaches. In this new approach, the importance of each background sample (or… CONTINUE READING
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