New Insights of Background Estimation and Region Localization

International Journal of Research and Engineering

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Title New Insights of Background Estimation and Region Localization
Creator Lin, Htet Htet
Description Subtraction of background in a crowded scene is a crucial and challenging task of monitoring the surveillance systems. Because of the similarity between the foreground object and the background, it is known that the background detection and moving foreground objects is difficult. Most of the previous works emphasize this field but they cannot distinguish the foreground from background due to the challenges of gradual or sudden illumination changes, high-frequencies background objects of motion changes, background geometry changes and noise. After getting the foreground objects, segmentation is need to localize the objects region. Image segmentation is a useful tool in many areas, such as object recognition, image processing, medical image analysis, 3D reconstruction, etc. In order to provide a reliable foreground image, a carefully estimated background model is needed. To tackle the issues of illumination changes and motion changes, this paper establishes an effective new insight of background subtraction and segmentation that accurately detect and segment the foreground people. The scene background is investigates by a new insight, namely Mean Subtraction Background Estimation (MS), which identifies and modifies the pixels extracted from the difference of the background and the current frame. Unlike other works, the first frame is calculated by MS instead of taking the first frame as an initial background. Then, this paper make the foreground segmentation in the noisy scene by foreground detection and then localize these detected areas by analyzing various segmentation methods. Calculation experiments on the challenging public crowd counting dataset achieve the best accuracy than state-of-the-art results. This indicates the effectiveness of the proposed work.
Publisher IJRE Publisher
Date 2019-01-04
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source International Journal of Research and Engineering; Vol 6 No 1 (2019): January 2019 Edition; 556-562
Language eng
Rights Copyright (c) 2019 Htet Htet Lin

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