Moving Object Detection: A New Method Combining Background Subtraction, Fuzzy Entropy Thresholding and Differential Evolution Optimization Cover

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Moving Object Detection: A New Method Combining Background Subtraction, Fuzzy Entropy Thresholding and Differential Evolution Optimization

Acta Mechanica et Automatica

Volume 19 (2025): Issue 1 (March 2025)

By: Oussama Boufares , Mohamed Boussif , Wajdi Saadaoui and Imed Miraoui

Open Access

|Mar 2025

Figures & Tables

General flow chart of the proposed method

The first row shows the original images, the second row shows the generated difference images

Change in intensity of the input image along the x-axis relative to the output image along the y-axis, when gamma is less than or equal to 1 and b gamma is greater than or equal to 1

The first row shows the original images, and the post-processing scheme improves the difference images

Flowchart of segmentation process

Fuzzy membership function for n - level segmentation

Comparative analysis of our approach with state-of-the-art methods by exploiting specific videos such as “HumanBody1-HB” and “HallAndMonitor-HM” from the SBI2015 dataset. The left-to-right layout shows results for: original, ground truth, DeepBS [27], SC_SOBS [25], SuBSENSE [24], GMM_Zivk [26}, as well as our method. The results for NThr=4 are displayed in this figure

Comparative analysis of our approach with state-of-the-art methods by exploiting specific videos such as “SnowFall-SF”, “BusStation-BS” and “Canoe-CE” from the CDnet 2014 dataset. The left-to-right layout shows results for original, ground truth, DeepBS [27], SC_SOBS [25], SuBSENSE [24], GMM_Zivk [26], as well as our method, The results for NThr=4 are displayed in this figure

Comparative analysis of our approach with state-of-the-art methods by exploiting specific videos such as “Highway-HG” and “Pedestrians-PD” from the CDnet 2014 dataset. The left-to-right layout shows results for original, ground truth, DeepBS [27], SC_SOBS [25], SuBSENSE [24], GMM_Zivk [26], as well as our method. The results for NThr=4 are displayed in this figure

Qualitative Performance of the MOD-BFDO Approach on I_BS_01 (Bootstrap, Moderate Shadows): (a) Original Image, (b) Grayscale Image, (c) Ground Truth, (d) Proposed Approach. The results for NThr=4 are displayed in this figure

Qualitative Performance of the MOD-BFDO Approach on O_SU_01 (Dynamic background, camouflage, hard shadows.): (a) Original Image, (b) background model, (c) Ground Truth, (d) Proposed Approach. The results for NThr =4 are displayed in this figure

Qualitative performance of the MOD-BFDO approach on the “111” synthetic videos from the BMC2012 dataset. This figure shows: (a) the original image, (b) the background model, and (c) the results obtained with the proposed approach. The results displayed correspond to NThr=4

Comparative assessment of F-measure across four categories using four methods on the LASIESTA dataset_ Each row presents the results for a specific method, while each column displays the average scores for each category

Methods	F-M
	I_SI	I_CA	I_BS	O_SU	Overall
GMM [31]	0.8328	0.8272	0.36941	0.7240	0.6880
GMM_	0.9054	08320	0.5330	0.7100	0.7450
Zivk [26]
Cuevas [32]	0.8805	0.8440	0.6809	0.8568	0.8155
Our approach	0.9089	0.8415	0.7021	0.8938	0.8390

Mean F-measure and standard deviations for different methods

Methods	Mean F-M (µ)	Standard Deviation (σ)
MOD-BFDO	0.8535	0.0920
SuBSENSE [24]	0.8257	0.1013
DeepBS [27]	0.8490	0.1296
SC_SOBS [25]	0.7158	0.1306
GMM_Zivk [26]	0.6696	0.1232

Results obtained by the proposed algorithm on the LASIESTA dataset

Category	RE	PWC	F-M	PR
I_SI	0.8969	0.5501	0.9089	0.9219
I_CA	0.7930	1.2835	0.8415	0.9250
I_BS	0.7015	0.4164	0.7120	0.7457
O_SU	0.8868	0.1917	0.8938	0.9038
Average	0.8195	0.6104	0.8390	0.8741

Z-scores for MOD-BFDO vs other methods

Comparison	z-Score
MOD-BFDO vs SuBSENSE	0.498
MOD-BFDO vs DeepBS	0.069
MOD-BFDO vs SC_SOBS	2.11
MOD-BFDO vs GMM_Zivk	2.93

Evaluation of our method on the CDnet 2014

Category	RE	SP	FPR	FNR	PWC	F-M	PR
Baseline	0.9577	0.9911	0.0021	0.0423	0.3634	0.9409	0.9432
Bad weather	0.8950	0.9970	0.0004	0.1053	0.5212	0.8834	0.8723
Dy. Backg	0.8839	0.9989	0,0013	0,2332	0,6121	0.9051	0.9272
Shadow	0,8704	0,9917	0,0082	0,1295	1,6663	0.8785	0,8869
Cam. Jitter	0.8154	0,9945	0,0057	0,1864	1,2627	0.8332	0.8515
Law. Fram	0.7610	0.9934	0.0061	0.2492	0.9064	0.6800	0.6146
Average	0.8639	0.9944	0.0039	0.1576	0.7220	0.8535	0.8492

Comparison of Average Frames Per Second (FPS) Across Three Source Video Sequences

Methods	Size of video
Methods	320×240	352×288	720×480
SC_SOBS [25]	9.8	8.7	3.4
SuBSENSE [24]	3.3	2.8	1.6
GMM _Zivk [26]	21.6	18.1	13.8
MOD-BFDO	5.5	4.7	3.2

Comparative assessment of F-measure in six categories using four methods_ Each row presents results specific to each method; each column displays the average scores in each category

Methods	F-M
Methods	Baseline	Bad weather	Dy. Backg	Shadow	Cam. Jitter	Law Fram	Overall
DeepBS [27]	0.9580	0.8301	0.8761	0.9304	0.8990	0.6002	0.8490
SC_SOBS [25]	0.9333	0.6620	0.6686	0.7786	0.7051	0.5463	0.7158
SuB-SENSE[24]	0.9503	0.8619	0.8177	0.8646	0.8152	0.6445	0.8257
GMM_Zivk [26]	0.8382	0.7406	0.6328	0.7322	0.5670	0.5065	0.6696
MOD-BFDO	0.9409	0.8834	0.9051	0.8785	0.8332	0.6800	0.8535

A comparison between our method and some of the most important existing methods on CDnet 2014 dataset

Methods	Overall
	Avg. RE	Avg. PR	Avg. PCW	Avg. F-M
DeepBS [27]	0.8312	0.8712	0.6373	0. 8490
SC_SOBS [25]	0.8068	0.7141	2.1462	0.7158
SuBSENSE [24]	0.8615	0.8606	0.8116	0.8257
GMM _Zivk [26]	0.7155	0.6722	1.7052	0.6696
MOD-BFDO	0.8639	0.8492	0.72202	0.8535

DOI: https://doi.org/10.2478/ama-2025-0013 | Journal eISSN: 2300-5319 | Journal ISSN: 1898-4088

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Language: English

Page range: 106 - 116

Submitted on: Mar 28, 2024

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Accepted on: Sep 25, 2024

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Published on: Mar 31, 2025

Published by: Bialystok University of Technology

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

detecting moving objects,

differential evolution,

object segmentation

Related subjects:

Electrical engineering,

Mechanical engineering,

Bioengineering and biomedical engineering,

Civil engineering,

Environmental engineering

© 2025 Oussama Boufares, Mohamed Boussif, Wajdi Saadaoui, Imed Miraoui, published by Bialystok University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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