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Robust Cross-Scene Foreground Segmentation in Surveillance Video

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conference contribution
posted on 09.07.2021, 00:33 authored by D Liang, Z Wei, H Sun, Huiyu Zhou
Training only one deep model for large-scale cross-scenevideo foreground segmentation is challenging due to the off-the-shelf deep learning based segmentor relies on scene-specific structural information. This results in deep mod-els that are scene-biased and evaluations that are scene-influenced.In this paper, we integrate dual modalities(foregrounds’ motion and appearance), and then eliminat-ing features without representativeness of foreground throughattention-module-guided selective-connection structures. It isin an end-to-end training manner and to achieve scene adap-tation in the plug and play style. Experiments indicate theproposed method significantly outperforms the state-of-the-art deep models and background subtraction methods in un-trained scenes – LIMU and LASIESTA. (Codes and datasetwill be available after the anonymous stage.)


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School of Informatics


IEEE International Conference on Multimedia and Expo (ICME) 2021 July 5-9, 2021


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IEEE International Conference on Multimedia and Expo


Institute of Electrical and Electronics Engineers (IEEE)

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