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Patch-Based Background Initialization in Heavily Cluttered Video
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DOI：10.1109/TIP.2009.2038652.png)
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Abstract
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In this paper, we propose a patch-based technique for robust background initialization that exploits both spatial and temporal consistency of the static background. The proposed technique is able to cope with heavy clutter, i.e, foreground objects that stand still for a considerable portion of time. First, the sequence is subdivided in patches that are clustered along the time-line in order to narrow down the number of background candidates. Then, a tessellation is grown incrementally by selecting at each step the best continuation of the current background. The method rests on sound principles in all its stages and only few, intelligible parameters are needed. Experimental results show that the proposed algorithm is effective and compares favorably with existing techniques.
Keywords:
Background initialization
background modeling
bootstrapping
content-based representation
motion segmentation
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