Abstract
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 significantly less tuning effort and shows a better robustness, as we will demonstrate in quantitative experiments on self-created and standard benchmarks. Finally, segmentation is improved significantly by integrating the probabilistic evidence provided by the local classifiers with a graph cut segmentation algorithm.
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Ulges, A., Breuel, T.M. (2008). A Local Discriminative Model for Background Subtraction. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_51
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DOI: https://doi.org/10.1007/978-3-540-69321-5_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69320-8
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