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A slew of motion detection methods have been proposed in recent years. The background includes some constraints such as changes in illumination, shadow, cluttered the background, scene change and speed of dance between hand gestures and body gestures are different. One of the most basic methods for background subtraction is temporal averaging. We looked at a new adaptive thresholding approach in this paper. To identify moving objects in video sequences, an adaptive thresholding is used. Depending upon the speed of the technique we proposed a Gaussian distribution technique. Gaussian distribution done background subtraction depending upon active pixels it differentiates whether it is a background or foreground. The background model’s update rate has been modified to be adaptive and determined by pixel difference. Our aim is to improve the method’s F-measure by making it more adaptable to various scene scenarios. The experiment results are shown and evaluated. The proposed method and the original method’s quality parameters are compared.
Mrs. Bhavana R.Maale. 2026. \u201cBackground Subtraction of an Indian Classical Dance Videos using Adaptive Temporal Averaging Method\u201d. Global Journal of Science Frontier Research - I: Interdisciplinary GJSFR-I Volume 22 (GJSFR Volume 22 Issue I1): .
Crossref Journal DOI 10.17406/GJSFR
Print ISSN 0975-5896
e-ISSN 2249-4626
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Total Score: 107
Country: India
Subject: Global Journal of Science Frontier Research - I: Interdisciplinary
Authors: Mrs. Bhavana R.Maale, Dr. Suvarna.Nandyal (PhD/Dr. count: 1)
View Count (all-time): 137
Total Views (Real + Logic): 1574
Total Downloads (simulated): 32
Publish Date: 2026 01, Fri
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A slew of motion detection methods have been proposed in recent years. The background includes some constraints such as changes in illumination, shadow, cluttered the background, scene change and speed of dance between hand gestures and body gestures are different. One of the most basic methods for background subtraction is temporal averaging. We looked at a new adaptive thresholding approach in this paper. To identify moving objects in video sequences, an adaptive thresholding is used. Depending upon the speed of the technique we proposed a Gaussian distribution technique. Gaussian distribution done background subtraction depending upon active pixels it differentiates whether it is a background or foreground. The background model’s update rate has been modified to be adaptive and determined by pixel difference. Our aim is to improve the method’s F-measure by making it more adaptable to various scene scenarios. The experiment results are shown and evaluated. The proposed method and the original method’s quality parameters are compared.
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