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Nested-Net: a deep nested network for background subtraction
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DOI：10.1007/s13735-023-00270-z.png)
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Abstract
En 中文
Background subtraction is one of the most highly regarded steps in computer vision, especially in video surveillance applications. Although various approaches have been proposed to cope with the different difficulties of this field, many of these methods have not been able to fully tackle complicated situations in realistic scenes due to their sensitivity to many challenges. This paper presents a deep nested background subtraction algorithm based on residual micro-autoencoder blocks. Hence, our method is implemented as a U-net like architecture with more skip connections. The nested network uses residual connections between these micro-autoencoders that can extract significant multi-scale features of a complex scene. We also test and prove that the proposed method can work in various challenging situations. A small set of training samples is included to train this end-to-end network. The experimental results demonstrate that our model outperforms other state-of-the-art methods on two well-known benchmark datasets: CDNet 2014 and SBI 2015.
Keywords:
Background subtraction (BS)
Foreground detection
Deep learning
Nested-Net
Micro-autoencoder
U-net
Journal
IF:
2.9
Papers:
280
Citations:
866
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Cited Papers
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