SAEN-BGS: Energy-efficient spiking autoencoder network for background subtraction
Background subtraction (BGS) is utilized to detect moving objects in a video and is commonly employed at the onset of object tracking and human recognition processes. Nevertheless, existing BGS techniques utilizing deep learning still encounter challenges with various background noises in videos, in...
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| Vydáno v: | Pattern recognition Ročník 169; s. 111792 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.01.2026
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| ISSN: | 0031-3203 |
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| Abstract | Background subtraction (BGS) is utilized to detect moving objects in a video and is commonly employed at the onset of object tracking and human recognition processes. Nevertheless, existing BGS techniques utilizing deep learning still encounter challenges with various background noises in videos, including variations in lighting, shifts in camera angles, and disturbances like air turbulence or swaying trees. To address this problem, we design a spiking autoencoder network, termed SAEN-BGS, based on noise resilience and time-sequence sensitivity of spiking neural networks (SNNs) to enhance the separation of foreground and background. To eliminate unnecessary background noise and preserve the important foreground elements, we begin by creating the continuous spiking conv-and-dconv block, which serves as the fundamental building block for the decoder in SAEN-BGS. Moreover, in striving for enhanced energy efficiency, we introduce a novel self-distillation spiking supervised learning method grounded in ANN-to-SNN frameworks, resulting in decreased power consumption. In extensive experiments conducted on CDnet-2014 and DAVIS-2016 datasets, our approach demonstrates superior segmentation performance relative to other baseline methods, even when challenged by complex scenarios with dynamic backgrounds.
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•To address the background noise, a spiking autoencoder network is developed using the noise resilience and time-sequence sensitivity of SNNs.•This is the first instance of solving background subtraction from a spike-based perspective, where a continuous spiking convolutional and deconvolutional block is employed to enhance foreground features and diminish background noise within the decoder.•To achieve energy efficiency, a novel self-distillation spiking supervised learning method is proposed within ANN-to-SNN framework.•The empirical evaluations on CDnet-2014 and DAVIS-2016 demonstrate the superiority of the proposed method. |
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| AbstractList | Background subtraction (BGS) is utilized to detect moving objects in a video and is commonly employed at the onset of object tracking and human recognition processes. Nevertheless, existing BGS techniques utilizing deep learning still encounter challenges with various background noises in videos, including variations in lighting, shifts in camera angles, and disturbances like air turbulence or swaying trees. To address this problem, we design a spiking autoencoder network, termed SAEN-BGS, based on noise resilience and time-sequence sensitivity of spiking neural networks (SNNs) to enhance the separation of foreground and background. To eliminate unnecessary background noise and preserve the important foreground elements, we begin by creating the continuous spiking conv-and-dconv block, which serves as the fundamental building block for the decoder in SAEN-BGS. Moreover, in striving for enhanced energy efficiency, we introduce a novel self-distillation spiking supervised learning method grounded in ANN-to-SNN frameworks, resulting in decreased power consumption. In extensive experiments conducted on CDnet-2014 and DAVIS-2016 datasets, our approach demonstrates superior segmentation performance relative to other baseline methods, even when challenged by complex scenarios with dynamic backgrounds.
[Display omitted]
•To address the background noise, a spiking autoencoder network is developed using the noise resilience and time-sequence sensitivity of SNNs.•This is the first instance of solving background subtraction from a spike-based perspective, where a continuous spiking convolutional and deconvolutional block is employed to enhance foreground features and diminish background noise within the decoder.•To achieve energy efficiency, a novel self-distillation spiking supervised learning method is proposed within ANN-to-SNN framework.•The empirical evaluations on CDnet-2014 and DAVIS-2016 demonstrate the superiority of the proposed method. |
| ArticleNumber | 111792 |
| Author | Zhang, Zhixuan Li, Xiao Peng Liu, Qi |
| Author_xml | – sequence: 1 givenname: Zhixuan orcidid: 0000-0002-3933-5401 surname: Zhang fullname: Zhang, Zhixuan email: zhangzhixuan77@gmail.com organization: School of Future Technology, South China University of Technology, Guangzhou, 511442, Guangdong, China – sequence: 2 givenname: Xiao Peng orcidid: 0000-0002-5448-7219 surname: Li fullname: Li, Xiao Peng email: x.p.li@szu.edu.cn organization: State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen, 518060, Guangdong, China – sequence: 3 givenname: Qi orcidid: 0000-0001-5378-6404 surname: Liu fullname: Liu, Qi email: drliuqi@scut.edu.cn organization: School of Future Technology, South China University of Technology, Guangzhou, 511442, Guangdong, China |
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| Keywords | Deep learning Energy efficiency Background subtraction Autoencoder Spiking neural network |
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