Segmentation of Moving Object Using Background Subtraction Method in Complex Environments

Kumar, Satrughan; Yadav, J. S.

doi:10.13164/re.2016.0399

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…For moving object detection, different approaches based on the difference in color distribution or pixel intensity have been proposed by researchers, e.g., [ 1 , 2 , 3 , 4 , 6 , 7 , 8 , 9 , 10 , 23 ], to eliminate the background in video frames. A widely used algorithm with low computational complexity is frame differencing [ 6 , 7 ], which utilizes the gray level difference between two or three adjacent video frames.…”

Section: Related Workmentioning

confidence: 99%

“…Existing methods for moving object detection include background subtraction [ 1 , 2 , 3 , 4 , 5 ], frame differencing [ 6 , 7 ], optical flow [ 8 , 9 , 10 ], ViBe [ 11 , 12 ] and deep learning [ 13 , 14 , 15 , 16 , 17 , 18 ]. Accuracies are adversely affected when using these methods to achieve real-time detection due to high image resolution and environmental complexities.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Moving Object Detection in High-Resolution Video Sensing

Zhu

Wei

et al. 2020

Sensors

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This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Moving Object Detection in High-Resolution Video Sensing

Zhu

Wei

et al. 2020

Sensors

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show abstract

“…Identification of moving objects can utilize a variety of combinations with the background subtraction method. Segmentation is one of the steps of the background subtraction technique [4]. Thresholding, also known as floating, is the process of converting a grayscale image into a binary image by altering all pixels with a value of zero when below the threshold and one when above the threshold [5].…”

Section: Introductionmentioning

confidence: 99%

Impact of Wolf Thresholding on Background Subtraction for Human Motion Detection

Pambudi,

Nurhidayat

2024

Compiler

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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, and running frame sequentially. Based on MSE and PSNR obtained on four videos, it has shown that wolf thresholding has succeeded in outperforming of global threshold.

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“…The techniques implemented by various researchers (Kumar & Yadav, 2016a) (Fazli et al, 2009a) (Fazli et al, 2009b) in these three stages define the accuracy and precision of the proposed algorithms. In our study, in the background modeling stage, an improved Expectation Maximization based algorithm for Gaussian mixtures is proposed to initialize the multi modal background with dynamic conditions.…”

Section: Introductionmentioning

confidence: 99%

Detection of Change in Body Motion With Background Construction and Silhouette Orientation

Mahajan

Padha

2022

International Journal of Information Retrieval Research

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Background subtraction techniques have been widely implemented and improvised to obtain a stable background model. The novelty of the proposed work is to generate a stable background model under dynamic changes in the environmental conditions where a) an improved background subtraction algorithm is proposed based on GMM with EM algorithm for computing granulometry and run faster for the generation of a stable background model; b) Detecting the foreground by curvelet based denoising process with improvised semisoft thresholding techniques with morphological operations; c) background maintenance is done by an adaptive algorithm in which the intensity values are mapped to remove the connected components with less than P pixels. The proposed scheme works for the Spatio-temporal motion of the object in both spatial and temporal modes. The experimental outcome for the proposed model results in the accurate shape analysis of the object in motion thereby dipping the complexity

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Segmentation of Moving Object Using Background Subtraction Method in Complex Environments

Cited by 9 publications

References 26 publications

Real-Time Moving Object Detection in High-Resolution Video Sensing

Real-Time Moving Object Detection in High-Resolution Video Sensing

Impact of Wolf Thresholding on Background Subtraction for Human Motion Detection

Detection of Change in Body Motion With Background Construction and Silhouette Orientation

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