In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every frame by controlling the learning rate according to the situations. We apply an adaptive threshold method based on image changes to effectively subtract the dynamic backgrounds. The devised scheme allows the proposed method to automatically adapt to various environments and effectively extract the foreground. The method presented here exhibits good performance and is suitable for dynamic background environments. The algorithm is tested on various video sequences and compared with other state-of-the-art background subtraction methods so as to verify its performance.
Jeisung Lee and Mignon Park used the first frame to initialize KDE and subsequently updated it at every frame by controlling the learning rate on the basis of image storage reduction REF .
An Adaptive Background Subtraction Method Based on Kernel Density Estimation
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Computer Science