Robust Background Modeling with Kernel Density Estimation

Modeling background and segmenting moving objects are significant techniques for video surveillance and other video processing applications. In this paper, we proposed a novel adaptive approach for modeling background and segmenting moving objects with a non-parametric kernel density estimation. Unlike previous approaches to object detection that detect objects by global thresholds, we used a local threshold to reflect temporal persistence. With a combination of global thresholds and local thresholds, the proposed approach can handle scenes containing gradual illumination variations and noise and has no bootstrapping limitations. Experimental results on different types of videos demonstrate the utility and performance of the proposed approach.