Cross-scene foreground segmentation with supervised and unsupervised model communication

Highlights：

• Coarse-to-fine segmentation. The proposed interaction of supervised and unsupervised models can realize fine-grained foreground segmentation.

• Unsupervised background model updates. The unsupervised statistical background model can update and avoid deadlock by using segmented masks as external selectiveupdating cues.

• This method is more flexible than deep learning-based methods that depend on scenespecific training. Compared with unsupervised models, it reduces the number of training samples and utilizes training datasets with no human intervention.