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- W2022034909 abstract "In this paper, a novel method GHoG is proposed based on Gaussian Mixture Model and Histogram of Oriented Gradients for human detection in a public places. The human region and non-human region feature vectors are extracted using Histogram of Oriented Gradients (HoG) and the feature vectors are trained using SVM classifier to detect and classify the human and non-human region. Now, Gaussian of Mixture model (GMM) is applied to the human detected region. Thus the human alone is segmented from the image, as a result we get the human postures. However, the background or the non-human region is not considered for segmentation and time to model the background is reduced. In our algorithm, we handle illumination variation and dynamic background. Our proposed method provides 96% detection accuracy for our video set and 100% detection accuracy for INRIA dataset." @default.
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- W2022034909 date "2013-12-01" @default.
- W2022034909 modified "2023-09-24" @default.
- W2022034909 title "Human detection in public environment using GHOG: Gaussian of mixtures &Histogram of Oriented Gradients" @default.
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- W2022034909 doi "https://doi.org/10.1109/icoac.2013.6921961" @default.
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