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Efficient hierarchical method for background subtraction
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DOI：10.1016/j.patcog.2006.11.023.png)
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摘要
En 中文
Detecting moving objects by using an adaptive back-round model is a critical component for many vision-based applications. Most background models were maintained in pixel-based forms, while some approaches began to study block-based representations which are more robust to non-stationary backgrounds. In this paper, we propose a method that combines pixel-based and block-based approaches into a single framework. We show that efficient hierarchical backgrounds can be built by considering that these two approaches are complementary to each other. In addition, a novel descriptor is proposed for block-based background modeling in the coarse level of the hierarchy. Quantitative evaluations show that the proposed hierarchical method can provide better results than existing single-level approaches. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keyword:
hierarchical background modeling
background subtraction
contrast histogram
non-stationary backgrounds object detection
video surveillance
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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