We propose a fast algorithm to estimate background models using Parzen density estimation in non-stationary scenes. Each pixel has a probability density which approximates pixel values observed in a video sequence. It is important to estimate a probability density function fast and accurately. In our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. The model adapts quickly to changes in the scene and foreground objects can be robustly detected. In addition, applying our approach to cast-shadow modeling, we can detect moving cast shadows. Several experiments show the effectiveness of our approach. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Tanaka, T., Shimada, A., Arita, D., & Taniguchi, R. I. (2007). Non-parametric background and shadow modeling for object detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4843 LNCS, pp. 159–168). Springer Verlag. https://doi.org/10.1007/978-3-540-76386-4_14
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