WisenetMD: Motion Detection Using Dynamic Background Region Analysis
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Abstract (arXiv)
Motion detection algorithms that can be applied to surveillance cameras such as CCTV (Closed Circuit Television) have been studied extensively. Motion detection algorithm is mostly based on background subtraction. One main issue in this technique is that false positives of dynamic backgrounds such as wind shaking trees and flowing rivers might occur. In this paper, we proposed a method to search for dynamic background region by analyzing the video and removing false positives by re-checking false positives. The proposed method was evaluated based on CDnet 2012/2014 dataset obtained at "changedetection.net" site. We also compared its processing speed with other algorithms.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2018.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 0; 2025: 1.
7 papers.
- 2021BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtractioncited by 15
- 2023ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selectioncited by 9
- 2021Autoencoder-based background reconstruction and foreground segmentation with background noise estimationcited by 7
- 2022Graph CNN for Moving Object Detection in Complex Environments from Unseen Videoscited by 4
- 2022Dynamic Background Subtraction by Generative Neural Networkscited by 3
- 2025SAEN-BGS: Energy-Efficient Spiking AutoEncoder Network for Background Subtractioncited by 2
- 2023Weakly Supervised Realtime Dynamic Background Subtractioncited by 0
- 2025SAEN-BGS: Energy-Efficient Spiking AutoEncoder Network for Background Subtraction12 May 2025
- 2023ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection26 Mar 2023
- 2023Weakly Supervised Realtime Dynamic Background Subtraction6 Mar 2023
- 2022Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos13 Jul 2022
- 2022Dynamic Background Subtraction by Generative Neural Networks10 Feb 2022
- 2021Autoencoder-based background reconstruction and foreground segmentation with background noise estimation15 Dec 2021
- 2021BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction23 Jan 2021