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- W4315705044 abstract "Moving object detection is still a challenging task in complex scenes. The existing methods based on deep learning mainly use U-Nets and have achieved amazing results. However, they ignore the local continuity between pixels. In order to solve this problem, a method based on a superpixel fusion network (SF-Net) is proposed in this article. First, the median filter is used to extract the candidate foreground (called pixel features ) and the image sequence is segmented by superpixel. Then, the histogram features (called superpixel features ) of the candidate foreground superpixels are extracted. Next, the pixel features and the superpixel features are the inputs of SF-Net, respectively. Experiments show the effectiveness of SF-Net on 34 image sequences and the average F-measure reaches 0.84. SF-Net can remove more background noise and has stronger expression ability than a network with the same depth." @default.
- W4315705044 created "2023-01-12" @default.
- W4315705044 creator A5064842058 @default.
- W4315705044 date "2023-03-16" @default.
- W4315705044 modified "2023-09-30" @default.
- W4315705044 title "Detection of Moving Object Using Superpixel Fusion Network" @default.
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- W4315705044 doi "https://doi.org/10.1145/3579998" @default.
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