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• W3200280803 abstract "Proper consideration of the temporal domain and the spatial domain is essential to perform robust foreground object detection in visual surveillance. However, there are difficulties in considering long-term temporal information with CNN-based methods. To solve this limitation, classical algorithms and some deep learning-based algorithms have used a background model image. However, acquiring a sophisticated background model image is also one of the complex problems. Most of the algorithms take a lot of time to initialize the background model image and generate many errors in the presence of a static foreground. This paper proposes an algorithm for generating a background model image using a deep-learning-based segmenter to solve this problem. The proposed method shows a 66.25% lower mean square error (MSE) than the background subtraction (BGS) algorithm and 79.25% lower than the latest deep learning algorithm in the SBI dataset. In addition, in the deep learning-based segmenter that uses a background image as input, replacing the background image of BGS algorithm with the background image of the proposed method shows a 38.63% reduction in the false detection rate (PWC)." @default.
• W3200280803 created "2021-09-27" @default.
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• W3200280803 date "2021-01-01" @default.
• W3200280803 modified "2023-09-29" @default.
• W3200280803 title "Generation of Background Model Image Using Foreground Model" @default.
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• W3200280803 doi "https://doi.org/10.1109/access.2021.3111686" @default.
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