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Proceedings Paper

Efficient background model based on multi-level feedback for video surveillance
Author(s): Song Tang; Bingshu Wang; Yong Zhao; Xuefeng Hu; Yuanzhi Gong
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Paper Abstract

Segmentation of moving objects from video sequences is the fundamental step in intelligent surveillance applications. Numerous methods have been proposed to obtain object segmentation. In this paper, we present an effective approach based on the mixture of Gaussians. The approach makes use of a feedback strategy with multiple levels: the pixel level, the region level, and the frame level. Pixel-level feedback helps to provide each pixel with an adaptive learning rate. The maintenance strategy of the background model is adjusted by region-level feedback based on tracking. Frame-level feedback is used to detect the global change in scenes. These different levels of feedback strategies ensure our approach’s effectiveness and robustness. This is demonstrated through experimental results on the Change Detection 2014 benchmark dataset.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100335L (29 August 2016); doi: 10.1117/12.2244495
Show Author Affiliations
Song Tang, Peking Univ. (China)
Bingshu Wang, Peking Univ. (China)
Yong Zhao, Peking Univ. (China)
Xuefeng Hu, Peking Univ. (China)
Yuanzhi Gong, Peking Univ. (China)

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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