Abstract
We propose a new method of background modeling for object detection. Many background models have been previously proposed, and they are divided into two types: “pixel-based models” which model stochastic changes in the value of each pixel and “spatial-based models” which model a local texture around each pixel. Pixel-based models are effective for periodic changes of pixel values, but they cannot deal with sudden illumination changes. On the contrary, spatial-based models are effective for sudden illumination changes, but they cannot deal with periodic change of pixel values, which often vary the textures. To solve these problems, we propose a new probabilistic background model integrating pixel-based and spatial-based models by considering the illumination fluctuation in localized regions. Several experiments show the effectiveness of our approach.
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© 2011 Springer-Verlag Berlin Heidelberg
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Yoshinaga, S., Shimada, A., Nagahara, H., Taniguchi, Ri. (2011). Object Detection Using Local Difference Patterns. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_18
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DOI: https://doi.org/10.1007/978-3-642-19282-1_18
Publisher Name: Springer, Berlin, Heidelberg
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