Background Pixel Clissification for Motion Segmentation using Mean Shift Algorithm

Adaptive background updating is an important step in motion segmentations of video sequences. However, the irregular distributions of background pixel values make the background modeling complicated. In this work, a method for background pixel classification based on the mean shift algorithm is proposed, which can classify the background pixels as single mode or multiple mode pixels so that different updating methods can be applied, for example, the single-mode pixel values can be updated with a simple and fast method such as IIR filter, while the multi-mode pixel values are modeled by a more complex updating algorithm such as a mixture of K Gaussian distributions or the non-parametric kernel estimation, which is robust to small motions (noisy motions, repetitive motions). Since in most scenes, the number of static background pixels (single-mode pixels) is far more than the number of dynamic background pixels (multi-mode pixels), thus the presented method can help improve the speed of background reconstruction without reducing its precision.