Joint domain-range modeling of dynamic scenes with adaptive Kernel bandwidth

Abstract

The first step in various computer vision applications is a detection of moving objects. The prevalent pixel-wise models regard image pixels as independent random processes. They don't take into account the existing correlation between the neighboring pixels. By using a non-parametric density estimation method over a joint domain-range representation of image pixels, this correlation can be exploited to achieve high levels of detection accuracy in the presence of dynamic backgrounds. This work improves recently proposed joint domain-range model for the background subtraction, which assumes the constant kernel bandwidth. The improvement is obtained by adapting the kernel bandwidth according to the local image structure. This approach provides the suppression of structural artifacts present in detection results when the kernel density estimation with constant bandwidth is used. Consequently, a more accurate detection of moving objects can be achieved. © Springer-Verlag Berlin Heidelberg 2007.