WisenetMD: Motion Detection Using Dynamic Background Region Analysis

Jeong-Eun Lim; Jin-Wook Shim; Jisang Yoo; Sang-Ha Lee; Soon-Chul Kwon

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WisenetMD: Motion Detection Using Dynamic Background Region Analysis

Sang-Ha Lee, Soon-Chul Kwon, Jin-Wook Shim, Jeong-Eun Lim, Jisang Yoo

Computer Vision and Pattern Recognition · Submitted 23 May 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

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arXiv ID1805.09277 (version 1)

Pith IDLFKJC6ZF, Pith's permanent record of this paper and its review

CitationJeong-Eun Lim, Jin-Wook Shim, Jisang Yoo, Sang-Ha Lee, Soon-Chul Kwon. "WisenetMD: Motion Detection Using Dynamic Background Region Analysis." arXiv:1805.09277 (2018). https://pith.science/paper/LFKJC6ZF

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,761 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.10 · human participants 0.02 · controlled comparison 0.57 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.93 · mentions code or data availability 0.03.

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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.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Motion detection algorithms that can be applied to surveillance cameras such as CCTV (Closed Circuit Television) have been studied extensively.	background 1.00	0.04	0.02	asserted 1.00
s1	Motion detection algorithm is mostly based on background subtraction.	background 1.00	0.05	0.03	asserted 1.00
s2	One main issue in this technique is that false positives of dynamic backgrounds such as wind shaking trees and flowing rivers might occur.	background 0.90	0.06	0.03	speculative 0.46
s3	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.	contribution 0.74	0.93	0.30	asserted 0.99
s4	The proposed method was evaluated based on CDnet 2012/2014 dataset obtained at "changedetection.net" site.	method 0.99	0.90	0.03	asserted 0.99
s5	We also compared its processing speed with other algorithms.	method 0.80	0.93	0.04	asserted 0.95

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.64.

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full text
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

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.

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References

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3 arXiv papers cite this in 8 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 1 sentence without a confident label

Compares with it 2 papers

To verify the segmentation performance and efficient energy consumption of our proposed SAEN-BGS, we compare it with the counterpart AEN-BGS, and several state-of-the-art methods, including ZBS [ref] , CwisarDH [ref] , Spectral-360 [ref] , FTSG [ref] , SuBSENSE [ref] , DeepBS [ref] , SemanticBGS [ref] , IUTIS-5 [ref] , BSUV-Net [ref] , IUTIS-5+SemanticBGS [ref] , BSUV-Net 2.0 [ref] , WisenetMD [ref] , RTSS [ref] , SWCD [ref] and PAWCS [ref] .

SAEN-BGS: Energy-Efficient Spiking AutoEncoder Network for Background Subtraction · Baselines · 1.00
Instead, we compare BSUV-Net with state-of-the-art unsupervised algorithms, namely SWCD [ref] , WisenetMD [ref] and PAWCS [ref] , which, by definition, are video-agnostic.

BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos · Quantitative Results · 1.00

Cites it as background 2 papers

PAWCS [ref] , SWCD [ref] and WisenetMD [ref] are considered to be state-of-the-art unsupervised BGS algorithms.

BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos · Introduction · 0.81
More recently, Isik et al. [ref] introduce SWCD, a pixel-wise sliding-window technique that updates the background model with a dynamic control system, while Lee et al. [ref] devise a multi-step algorithm to decrease false positives in dynamic backgrounds.

SAEN-BGS: Energy-Efficient Spiking AutoEncoder Network for Background Subtraction · Background Subtraction Method · 0.96

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