A Local Discriminative Model for Background Subtraction

@inproceedings{Ulges2008ALD,
  title={A Local Discriminative Model for Background Subtraction},
  author={Adrian Ulges and Thomas M. Breuel},
  booktitle={DAGM-Symposium},
  year={2008}
}
Conventional background subtraction techniques that update a background model online have difficulties with correctly segmenting foreground objects if sudden brightness changes occur. Other methods that learn a global scene model offline suffer from projection errors. To overcome these problems, we present a different approach that is local and discriminative, i.e. for each pixel a classifier is trained to decide whether the pixel belongs to the background or foreground. Such a model requires… CONTINUE READING

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