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• W2972201472 abstract "Multi-modal foreground detection, which integrates multiple complementary data like visible and thermal infrared sources for moving object detection, has received more and more attention recently. In this paper, we propose a novel Multi-modal Foreground Detection approach that pursues the inter- and intra-modality consistencies in a unified Low-rank and Sparse separation model called MFDLS. In particular, we first introduce a soft cross-modal constraint to pursue the inter-modal consistency among different modalities, while allowing sparse inconsistency to model their heterogeneous properties. Second, in addition to the conventional local appearance consistency within each modality, we further propose to preserve the global appearance consistency via Gaussian Mixture Model as the intra-modality consistency. Extensive experimental results yield to a new state-of-the-art comparing to the prevalent multi-modal foreground detection methods." @default.
• W2972201472 created "2019-09-12" @default.
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• W2972201472 date "2020-01-01" @default.
• W2972201472 modified "2023-10-16" @default.
• W2972201472 title "Multi-modal foreground detection via inter- and intra-modality-consistent low-rank separation" @default.
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• W2972201472 doi "https://doi.org/10.1016/j.neucom.2019.08.087" @default.
• W2972201472 hasPublicationYear "2020" @default.
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