# WisenetMD: Motion Detection Using Dynamic Background Region Analysis

arXiv: https://arxiv.org/abs/1805.09277 · HTML: https://mlsanity.com/paper/1805.09277/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2018.

## Built on by (most cited first, 7 of 7)

- [BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction](https://mlsanity.com/paper/2101.09585/) (2021)
- [ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection](https://mlsanity.com/paper/2303.14679/) (2023)
- [Autoencoder-based background reconstruction and foreground segmentation with background noise estimation](https://mlsanity.com/paper/2112.08001/) (2021)
- [Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos](https://mlsanity.com/paper/2207.06440/) (2022)
- [Dynamic Background Subtraction by Generative Neural Networks](https://mlsanity.com/paper/2202.05336/) (2022)
- [SAEN-BGS: Energy-Efficient Spiking AutoEncoder Network for Background Subtraction](https://mlsanity.com/paper/2505.07336/) (2025)
- [Weakly Supervised Realtime Dynamic Background Subtraction](https://mlsanity.com/paper/2303.02857/) (2023)

## 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.