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Background Subtraction Using Dominant Directional Pattern
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AI
DOI：10.1109/ACCESS.2018.2846749.png)
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摘要
En 中文
Background subtraction technique is commonly used in foreground segmentation problem. Most of the existing color or intensity feature-based background subtraction methods suffer in non-stationary environments, such as presence of illumination variations. To address the issue, we introduce a new spatial feature descriptor that extracts the prominent directional information in a local neighborhood of a pixel. We use this local feature along with color information as the core component of a sample consensus-based model evolved from visual background extractor method. We also introduce an adaptive way to fuse color and local feature at each pixel to determine if the current observed features match to its corresponding background model while classification. The discriminative nature of our proposed local feature descriptor makes our model robust against the change of illumination. Extensive experiments on CDnet 2012 (changedetection.net ) data set demonstrate that our background subtraction method outperforms many state-of-the-art methods.
Keyword:
Background modeling
background subtraction
dominant directional pattern
features fusion
moving object segmentation