Abstract
We present a robust background model for object detection and report its evaluation results using the database of Background Models Challenge (BMC). Our background model is based on a statistical local feature. In particular, we use an illumination invariant local feature and describe its distribution by using a statistical framework. Thanks to the effectiveness of the local feature and the statistical framework, our method can adapt to both illumination and dynamic background changes. Experimental results, which are done thanks to the database of BMC, show that our method can detect foreground objects robustly against background changes.
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Yoshinaga, S., Shimada, A., Nagahara, H., Taniguchi, Ri. (2013). Background Model Based on Statistical Local Difference Pattern. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37410-4_30
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DOI: https://doi.org/10.1007/978-3-642-37410-4_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37409-8
Online ISBN: 978-3-642-37410-4
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