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Journal articles on the topic 'Adaptive thresholding background subtraction'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Pambudi, Elindra Ambar, and Muhammad Ivan Nurhidayat. "Impact of Wolf Thresholding on Background Subtraction for Human Motion Detection." Compiler 13, no. 1 (2024): 39. http://dx.doi.org/10.28989/compiler.v13i1.2116.

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Series of motion detection based on background subtraction there is an image segmentation stage. Thresholding is a common technique used for the segmentation process. There are two types that can be used in thresholding techniques namely local and global. This research intends to implement local adaptive wolf thresholding as the threshold value of the background subtraction method to detect motion objects. The proposed method consists of the reading frame, background and foreground initialization of each frame, preprocessing, background subtraction, wolf thresholding, providing a bounding box,

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Soundrapandiyan, Rajkumar, and P. V. S. S. R. Chandra Mouli. "Adaptive Pedestrian Detection in Infrared Images Using Background Subtraction and Local Thresholding." Procedia Computer Science 58 (2015): 706–13. http://dx.doi.org/10.1016/j.procs.2015.08.091.

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3

ZHANG, SHENGPING, HONGXUN YAO, and SHAOHUI LIU. "DYNAMIC BACKGROUND SUBTRACTION BASED ON LOCAL DEPENDENCY HISTOGRAM." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 07 (2009): 1397–419. http://dx.doi.org/10.1142/s0218001409007569.

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Traditional background subtraction methods perform poorly when scenes contain dynamic backgrounds such as waving tree branches, spouting fountain, illumination changes, camera jitters, etc. In this paper, from the view of spatial context, we present a novel and effective dynamic background method with three contributions. First, we present a novel local dependency descriptor, called local dependency histogram (LDH), to effectively model the spatial dependencies between a pixel and its neighboring pixels. The spatial dependencies contain substantial evidence for differentiating dynamic backgrou

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Et. al., Latha Anuj ,. "V-DaT: A Robust method for Vehicle Detection and Tracking." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2492–505. http://dx.doi.org/10.17762/turcomat.v12i2.2092.

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Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work, a method for vehicle detection and tracking is proposed. The proposed model considers background subtraction concept for moving vehicle detection but unlike conventional approaches, here numerous algorithmic optimization approaches have been applied such as multi-directional filtering and fusion based background subtraction, thresholding, directional filtering

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Solichin, A., A. A. Salman, and Painem. "Automated waste detection system for river surveillance in Jakarta using background substraction method." Journal of Physics: Conference Series 2866, no. 1 (2024): 012036. http://dx.doi.org/10.1088/1742-6596/2866/1/012036.

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Abstract In Jakarta, the issue of river pollution due to indiscriminate waste disposal poses serious environmental and safety concerns, often leading to flooding during the rainy season. Manual surveillance by human resources has proven ineffective in addressing the escalating scale of the problem. This study presents an automated waste detection system for river surveillance in Jakarta, especially inorganic waste, utilizing video processing techniques, specifically background subtraction and frame differencing. We collected and analyzed video data from 13 rivers, including the Ciliwung, Angke

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Dadavali S P. "Background Subtraction-based CNN with Dual-energy Dependent Active Contour for the Extraction and Classification of Skin Lesion Descriptors." Journal of Electrical Systems 20, no. 11s (2024): 3831–50. https://doi.org/10.52783/jes.8273.

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This work proposes a background subtraction-based convolutional neural network (BS-CNN) for extracting and classifying the color and texture descriptors of the skin lesion region. The proposed skin lesion classification approach has three phases namely preprocessing, lesion segmentation, and descriptor extraction with classification process. In the preprocessing phase, the skin lesion images are enhanced using contrast-limited adaptive histogram equalization, while the hair artifacts present in the image are removed using the morphological and thresholding process. In the second phase, the les

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Hasan, Thabit Rashid Kurmasha, and Hadi Al Israa. "Threshold adaptation and XOR accumulation algorithm for objects detection." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 2517–25. https://doi.org/10.11591/ijece.v12i3.pp2517-2525.

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Object detection, tracking and video analysis are vital and energetic tasks for intelligent video surveillance systems and computer vision applications. Object detection based on background modelling is a major technique used in dynamically objects extraction over video streams. This paper presents the threshold adaptation and XOR accumulation (TAXA) algorithm in three systematic stages throughout video sequences. First, the continuous calculation, updating and elimination of noisy background details with hybrid statistical techniques. Second, thresholds are calculated with an effective mean a

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Zhou, Xiaogen, Zuoyong Li, Huosheng Xie, et al. "Leukocyte Image Segmentation Based on Adaptive Histogram Thresholding and Contour Detection." Current Bioinformatics 15, no. 3 (2020): 187–95. http://dx.doi.org/10.2174/1574893614666190723115832.

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Aims: The proposed method falls into the category of medical image processing. Background: Computer-aided automatic analysis systems for the analysis and cytometry of leukocyte (White Blood Cells, WBCs) in human blood smear images are a powerful diagnostic tool for many types of diseases, such as anemia, malaria, syphilis, heavy metal poisoning, and leukemia. Leukocyte segmentation is a basis of its automatic analysis, and the segmentation accuracy will directly influence the reliability of image-based automatic leukocyte analysis. Objective: This paper aims to present a leukocyte segmentation

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Li, Jiahong, Xinkai Xu, Zhuoying Jiang, and Beiyan Jiang. "Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation." Applied Sciences 14, no. 3 (2024): 1045. http://dx.doi.org/10.3390/app14031045.

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Real-time visual object tracking (VOT) may suffer from performance degradation and even divergence owing to inaccurate noise statistics typically engendered by non-stationary video sequences or alterations in the tracked object. This paper presents a novel adaptive Kalman filter (AKF) algorithm, termed AKF-ALS, based on the autocovariance least square estimation (ALS) methodology to improve the accuracy and robustness of VOT. The AKF-ALS algorithm involves object detection via an adaptive thresholding-based background subtraction technique and object tracking through real-time state estimation

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Roshan, A., and Y. Zhang. "MOVING OBJECT DETECTION USING SPATIAL CORRELATION IN LAB COLOUR SPACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 173–77. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-173-2019.

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<p><strong>Abstract.</strong> Background subtraction-based techniques of moving object detection are very common in computer vision programs. Each technique of background subtraction employs image thresholding algorithms. Different thresholding methods generate varying threshold values that provide dissimilar moving object detection results. A majority of background subtraction techniques use grey images which reduce the computational cost but statistics-based image thresholding methods do not consider the spatial distribution of pixels. In this study, authors have developed

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Sang, Nong, Heng Li, Weixue Peng, and Tianxu Zhang. "Knowledge-based adaptive thresholding segmentation of digital subtraction angiography images." Image and Vision Computing 25, no. 8 (2007): 1263–70. http://dx.doi.org/10.1016/j.imavis.2006.07.026.

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Pambudi, Elindra Ambar, Abid Yanuar Badarudin, and Dimara Kusuma Hakim. "Analysis Thresholding Sauvola pada Background Subtraction untuk Deteksi Objek Bergerak." Jurnal Informatika 6, no. 2 (2019): 300–304. http://dx.doi.org/10.31311/ji.v6i2.6164.

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Metode segmentasi kebanyakan digunakan dalam teknik pengolahan citra yang berkaitan dengan deteksi objek bergerak. Segmentasi pada objek bergerak sangat penting untuk menentukan proses selanjutnya berupa pengenalan atau klasifikasi. Metode yang paling umum digunakan dalam teknik-teknik segmentasi adalah metode pengambangan. Metode pengambangan dibagi menjadi dua yaitu global dan lokal. Pada penelitian kali ini akan mencoba menggunakan salah satu metode pengambangan lokal adaptif yaitu sauvola. Sauvola akan digunakan sebagai nilai ambang dari background subtraction. Garis besar dari metode yang

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McHugh, J. Mike, Janusz Konrad, Venkatesh Saligrama, and Pierre-Marc Jodoin. "Foreground-Adaptive Background Subtraction." IEEE Signal Processing Letters 16, no. 5 (2009): 390–93. http://dx.doi.org/10.1109/lsp.2009.2016447.

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Jiang, Bowu, and Wenkai Lu. "Adaptive Multiple Subtraction Based on an Accelerating Iterative Curvelet Thresholding Method." IEEE Transactions on Image Processing 30 (2021): 806–21. http://dx.doi.org/10.1109/tip.2020.3038519.

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Liu, Fei, Xiaodan Song, Yupin Luo, and Dongcheng Hu. "Adaptive thresholding based on variational background." Electronics Letters 38, no. 18 (2002): 1017. http://dx.doi.org/10.1049/el:20020728.

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16

Sivabalakrishnan, M., and D. Manjula. "Adaptive background subtraction using fuzzy logic." International Journal of Multimedia Intelligence and Security 1, no. 4 (2010): 392. http://dx.doi.org/10.1504/ijmis.2010.039239.

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Boufares, Oussama, Mohamed Boussif, Wajdi Saadaoui, and Imed Miraoui. "Moving Object Detection: A New Method Combining Background Subtraction, Fuzzy Entropy Thresholding and Differential Evolution Optimization." Acta Mechanica et Automatica 19, no. 1 (2025): 106–16. https://doi.org/10.2478/ama-2025-0013.

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Abstract Detecting moving objects in videos is an evolving area of research, with important implications in many computer vision applications. In this paper, we propose a new detection approach by combining background subtraction and multi-level image thresholding based on fuzzy entropy, powered by the differential evolution (DE) algorithm. The first step of our method is background subtraction, aiming to isolate moving objects by eliminating the static background. However, this approach can be sensitive to lighting variations and background changes, thus limiting its accuracy. To overcome the

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CALZADA-NAVARRETE, V., and C. TORRES-HUITZIL. "A LOCAL ADAPTIVE THRESHOLD APPROACH TO ASSIST AUTOMATIC CHROMOSOME IMAGE SEGMENTATION." Latin American Applied Research - An international journal 44, no. 3 (2014): 277–82. http://dx.doi.org/10.52292/j.laar.2014.452.

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In cytogenetics, karyotype analysis is used to assess the presence of genetic defects by visualization chromosomes structure from microscopic images. A key step in this process is image thresholding, used to detect and extract objects of interest from background, as it affects the performance of further processing steps in image analysis. In this paper, an adaptive local thresholding for Qband chromosome image segmentation is presented. A re-threshold process based on the Sauvola’s local adaptive technique is applied to extract chromosomes from background. Local adaptive histogram equalization

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De-gui, Xiao, Yu Sheng-sheng, and Zhou Jing-li. "Motion tracking with fast adaptive background subtraction." Wuhan University Journal of Natural Sciences 8, no. 1 (2003): 35–40. http://dx.doi.org/10.1007/bf02902061.

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Reitberger, Günther, and Tomas Sauer. "Background Subtraction using Adaptive Singular Value Decomposition." Journal of Mathematical Imaging and Vision 62, no. 8 (2020): 1159–72. http://dx.doi.org/10.1007/s10851-020-00967-4.

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Abstract An important task when processing sensor data is to distinguish relevant from irrelevant data. This paper describes a method for an iterative singular value decomposition that maintains a model of the background via singular vectors spanning a subspace of the image space, thus providing a way to determine the amount of new information contained in an incoming frame. We update the singular vectors spanning the background space in a computationally efficient manner and provide the ability to perform blockwise updates, leading to a fast and robust adaptive SVD computation. The effects of

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Chiranjeevi, P., and S. Sengupta. "Spatially correlated background subtraction, based on adaptive background maintenance." Journal of Visual Communication and Image Representation 23, no. 6 (2012): 948–57. http://dx.doi.org/10.1016/j.jvcir.2012.06.004.

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Senthilkumaran, N1 and Vaithegi S2. "TOP 1 CITED PAPER - COMPUTER SCIENCE & ENGINEERING: AN INTERNATIONAL JOURNAL (CSEIJ)." COMPUTER SCIENCE & ENGINEERING: AN INTERNATIONAL JOURNAL (CSEIJ) 6, no. 1 (2019): 3. https://doi.org/10.5281/zenodo.3386005.

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Image binarization is the process of separation of pixel values into two groups, black as background and white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This paper describes a locally adaptive thresholding technique that removes background by using local mean and standard deviation. Most common and simplest approach to segment an image is using thresholding. In this work we present an efficient implementation for threshoding and give a detailed comparison of Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding

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Setiadi, De Rosal Ignatius Moses, Rizki Ramadhan Fratama, and Nurul Diyah Ayu Partiningsih. "Improved Accuracy of Vehicle Counter for Real-Time Traffic Monitoring System." Transport and Telecommunication Journal 21, no. 2 (2020): 125–33. http://dx.doi.org/10.2478/ttj-2020-0010.

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AbstractThis research proposes a background subtraction method with the truncate threshold to improve the accuracy of vehicle detection and tracking in real-time video streams. In previous research, vehicle detection accuracy still needs to be optimized, so it needed to be improved. In the vehicle detection method, there are several parts that greatly affect, one of which is the thresholding technique. Different thresholding methods can affect the results of the background and foreground separation. Based on the results of testing the proposed method can improve accuracy by more than 20% compa

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Li, Zhong-Xiao, and Zhen-Chun Li. "Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction." GEOPHYSICS 83, no. 2 (2018): V99—V113. http://dx.doi.org/10.1190/geo2016-0384.1.

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After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational co

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Sharma, Prabhat, Bambam Kumar, and Dharmendra Singh. "Development of Adaptive Threshold and Data Smoothening Algorithm for GPR Imaging." Defence Science Journal 68, no. 3 (2018): 316. http://dx.doi.org/10.14429/dsj.68.12354.

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There are many approaches available to separate the background and foreground in image processing applications. Currently, researchers are focusing on wavelet De-noising, curvelet threshold, Edge Histogram Descriptor threshold, Otsu thresholding, recursive thresholding and adaptive progressive thresholding. In fixed and predictable background conditions, above techniques separate background and foreground efficiently. In a common scenario, background reference is blind due to soil surface moisture content and its non-linearity. There are many methodologies proposed from time to time by researc

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Saddami, Khairun, Khairul Munadi, Yuwaldi Away, and Fitri Arnia. "Improvement of binarization performance using local otsu thresholding." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 264. http://dx.doi.org/10.11591/ijece.v9i1.pp264-272.

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<p><span>Ancient document usually contains multiple noises such as uneven-background, show-through, water-spilling, spots, and blur text. The noise will affect the binarization process. Binarization is an extremely important process in image processing, especially for character recognition. This paper presents an improvement to Nina binarization technique. Improvements were achieved by reducing processing steps and replacing median filtering by Wiener filtering. First, the document background was approximated by using Wiener filter, and then image subtraction was applied. Furthermo

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Saddami, Khairun, Khairul Munadi, Yuwaldi Away, and Fitri Arnia. "Improvement of binarization performance using local otsu thresholding." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 264–72. https://doi.org/10.11591/ijece.v9i1.pp264-272.

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Ancients document usually contains multiple noises such as uneven background, show-through, water-spilling, spots, and blur text. The noise will affect the binarization process. Binarization is an extremely important process in image processing, especially for character recognition. This paper presents an improvement to Nina binarization technique. Improvements were achieved by reducing processing steps and replacing median filtering by Wiener filtering. First, the document background was approximated by using a Wiener filter, and then image subtraction was applied. Furthermore, the manuscript

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De Stefano, G., and O. V. Vasilyev. "A fully adaptive wavelet-based approach to homogeneous turbulence simulation." Journal of Fluid Mechanics 695 (February 8, 2012): 149–72. http://dx.doi.org/10.1017/jfm.2012.6.

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AbstractThe ability of wavelet multi-resolution analysis to detect and track the energy-containing motions that govern the dynamics of a fluid flow offers a unique hierarchical framework for modelling and simulating turbulence. In this paper, the role of the wavelet thresholding level in wavelet-based modelling and simulation of turbulent flows is systematically examined. The thresholding level controls the relative importance of resolved energetic structures and residual unresolved background flow and, thus, the achieved turbulence resolution. A fully adaptive eddy capturing approach is devel

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Chen, Bing Wen, and Shi Long Liu. "Infrared Target Detection Based on Temporal-Spatial Domain Fusion." Advanced Materials Research 1044-1045 (October 2014): 1186–89. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1186.

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In order to improve the accuracy and stability of infrared target detection, a novel moving target detection approach based on temporal-spatial domain fusion is presented. A multi-level spatial-temporal median filter is utilized to extract the background frame, with which the background clutters are suppressed by using the background subtraction technique. Then a local weighted operator is applied to enhance the targets. Lastly, the otsu thresholding algorithm is utilized to detect the targets. Experimental results demonstrate that the proposed approach is capable of detecting infrared moving

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Weakley, Andrew T., Peter R. Griffiths, and D. Eric Aston. "Automatic Baseline Subtraction of Vibrational Spectra Using Minima Identification and Discrimination via Adaptive, Least-Squares Thresholding." Applied Spectroscopy 66, no. 5 (2012): 519–29. http://dx.doi.org/10.1366/110-06526.

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A method of automated baseline correction has been developed and applied to Raman spectra with a low signal-to-noise ratio and surface-enhanced infrared absorption (SEIRA) spectra with bipolar bands. Baseline correction is initiated by dividing the raw spectrum into equally spaced segments in which regional minima are located. Following identification, the minima are used to generate an intermediate second-derivative spectrum where points are assigned as baseline if they reside within a locally defined threshold region. The threshold region is similar to a confidence interval encountered in st

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Liu, De Fang, Ming Deng, and Dai Mu Wang. "Background Subtraction Based on Gaussian Mixture Model." Advanced Materials Research 694-697 (May 2013): 2021–26. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2021.

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According to the detection of moving objects in video sequences, the paper puts forward background subtraction based on Gauss mixture model. It analyzes the usual pixel-level approach, and to develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.

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Gu, Irene Yu-Hua. "Adaptive background subtraction based on feedback from fuzzy classification." Optical Engineering 43, no. 10 (2004): 2381. http://dx.doi.org/10.1117/1.1788694.

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Zhang, Ruolin, and Jian Ding. "Object Tracking and Detecting Based on Adaptive Background Subtraction." Procedia Engineering 29 (2012): 1351–55. http://dx.doi.org/10.1016/j.proeng.2012.01.139.

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Xie, Yi, Lidong Yang, Xilong Sun, et al. "An auto-adaptive background subtraction method for Raman spectra." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 161 (May 2016): 58–63. http://dx.doi.org/10.1016/j.saa.2016.02.016.

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Zeng, Zhi, Jianyuan Jia, Zhaofei Zhu, and Dalin Yu. "Adaptive maintenance scheme for codebook-based dynamic background subtraction." Computer Vision and Image Understanding 152 (November 2016): 58–66. http://dx.doi.org/10.1016/j.cviu.2016.08.009.

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KHASHMAN, ADNAN, and BORAN SEKEROGLU. "DOCUMENT IMAGE BINARISATION USING A SUPERVISED NEURAL NETWORK." International Journal of Neural Systems 18, no. 05 (2008): 405–18. http://dx.doi.org/10.1142/s0129065708001671.

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Advances in digital technologies have allowed us to generate more images than ever. Images of scanned documents are examples of these images that form a vital part in digital libraries and archives. Scanned degraded documents contain background noise and varying contrast and illumination, therefore, document image binarisation must be performed in order to separate foreground from background layers. Image binarisation is performed using either local adaptive thresholding or global thresholding; with local thresholding being generally considered as more successful. This paper presents a novel m

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Pratomo, Awang Hendrianto, Wilis Kaswidjanti, Alek Setiyo Nugroho, and Shoffan Saifullah. "Parking detection system using background subtraction and HSV color segmentation." Bulletin of Electrical Engineering and Informatics 10, no. 6 (2021): 3211–19. http://dx.doi.org/10.11591/eei.v10i6.3251.

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Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter).

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Awang, Hendrianto Pratomo, Kaswidjanti Wilis, Setiyo Nugroho Alek, and Saifullah Shoffan. "Parking detection system using background subtraction and HSV color segmentation." Bulletin of Electrical Engineering and Informatics 10, no. 6 (2021): 3211–19. https://doi.org/10.11591/eei.v10i6.3251.

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Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter).

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Hung, Chia-Shao, and Shanq-Jang Ruan. "Efficient adaptive thresholding algorithm for in-homogeneous document background removal." Multimedia Tools and Applications 75, no. 2 (2014): 1243–59. http://dx.doi.org/10.1007/s11042-014-2366-7.

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Liu, Rongrong, Yassine Ruichek, and Mohammed El-Bagdouri. "Extended Codebook with Multispectral Sequences for Background Subtraction." Sensors 19, no. 3 (2019): 703. http://dx.doi.org/10.3390/s19030703.

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: The Codebook model is one of the popular real-time models for background subtraction. In this paper, we first extend it from traditional Red-Green-Blue (RGB) color model to multispectral sequences. A self-adaptive mechanism is then designed based on the statistical information extracted from the data themselves, with which the performance has been improved, in addition to saving time and effort to search for the appropriate parameters. Furthermore, the Spectral Information Divergence is introduced to evaluate the spectral distance between the current and reference vectors, together with the

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Prahara, Adhi, Andri Pranolo, Nuril Anwar, and Yingchi Mao. "Parallel Approach of Adaptive Image Thresholding Algorithm on GPU." Knowledge Engineering and Data Science 4, no. 2 (2022): 69. http://dx.doi.org/10.17977/um018v4i22021p69-84.

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Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational

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Tripathy, D. P., and K. Guru Raghavendra Reddy. "ADAPTIVE THRESHOLD BACKGROUND SUBTRACTION FOR DETECTING MOVING OBJECT ON CONVEYOR BELT." International Journal of Students' Research in Technology & Management 5, no. 4 (2017): 46–51. http://dx.doi.org/10.18510/ijsrtm.2017.546.

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Moving object detection is an important task in many computer vision classifications applications. The goal of this study is to identify a moving object detection method that provides a reliable and accurate identification of objects on the conveyor belt. In this paper, a study of the moving object detection methods is presented. Firstly, moving object detection pixel by pixel was performed using background subtraction, frame difference method. The threshold value in both background subtraction and frame difference is a fixed value, which determines the accuracy of object identification. The a

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Yazid, H., M. H. Mat Som, S. N. Basah, S. Abdul Rahim, M. F. Mahmud, and H. Arof. "Performance Analysis on the Effect of Noise in Inverse Surface Adaptive Thresholding (ISAT)." Journal of Physics: Conference Series 2071, no. 1 (2021): 012031. http://dx.doi.org/10.1088/1742-6596/2071/1/012031.

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Abstract Thresholding is one of the powerful methods in segmentation phase. Numerous methods were proposed to segment the foreground from the background but there is limited number of studies that analyse the effect of noise since the present of noise will affect the performance of the thresholding method. In this paper, the main idea is to analyse the effect of noise in Inverse Surface Adaptive Thresholding (ISAT) method. ISAT method is known as an excellent method to segment the image with the present of noise. The result of this analysis can be a guideline to researcher when implementing IS

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Nallasivam, Manikandaprabu, and Vijayachitra S. "Advanced Perspective on Human Detection system with Hybrid Feature Set." U.Porto Journal of Engineering 8, no. 6 (2022): 178–88. http://dx.doi.org/10.24840/2183-6493_008.006_0013.

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Detecting and discriminating humans in video frames for surveillance applications is a demanding task. Identifying and highlighting humans by eliminating shadows from the video frames is vital for prudence motive. In this paper, a three-step procedure is proposed, which includes motion detection by background subtraction in live video frames, morphological gradient-based shadow removal, and human detection by Hybrid Feature Set (HFS), which comprises Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) with adaptive Neuro-Fuzzy inference system. The first step incorporates static b

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Wang, Jianming, and Jianhua Chen. "An Improved Background Subtraction Method for Adaptive Rate Compressive Sensing." Journal of Physics: Conference Series 1914, no. 1 (2021): 012024. http://dx.doi.org/10.1088/1742-6596/1914/1/012024.

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BEAUGENDRE, Axel, and Satoshi GOTO. "Adaptive Block-Propagative Background Subtraction Method for UHDTV Foreground Detection." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E98.A, no. 11 (2015): 2307–14. http://dx.doi.org/10.1587/transfun.e98.a.2307.

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Unzueta, Luis, Marcos Nieto, Andoni Cortes, Javier Barandiaran, Oihana Otaegui, and Pedro Sanchez. "Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification." IEEE Transactions on Intelligent Transportation Systems 13, no. 2 (2012): 527–40. http://dx.doi.org/10.1109/tits.2011.2174358.

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Lu, Shan, and Xianmin Ma. "Adaptive random-based self-organizing background subtraction for moving detection." International Journal of Machine Learning and Cybernetics 11, no. 6 (2019): 1267–76. http://dx.doi.org/10.1007/s13042-019-01037-x.

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Lee, Jeisung, and Mignon Park. "An Adaptive Background Subtraction Method Based on Kernel Density Estimation." Sensors 12, no. 9 (2012): 12279–300. http://dx.doi.org/10.3390/s120912279.

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Kim, H., B. Ku, D. K. Han, S. Kang, and H. Ko. "Adaptive selection of model histograms in block-based background subtraction." Electronics Letters 48, no. 8 (2012): 434. http://dx.doi.org/10.1049/el.2011.4068.

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