Video Segmentation Based on Spatial-temporal Attention Model
基于时空注意模型的视频分割算法

To deal with the error segmentation problem of the existing video algorithms under complex and dynamic scenes, the proposed method extracts spatial-temporal attention features with salient maps, and adopts hierarchical conditional random field for video segmentation. Firstly, the algorithm constructs a weighted combination model based on spatial-temporal features by using information theory. Then, it uses the defined model to compute probability distribution of salient maps, which can locate region of moving object effectively. Finally, the Gaussian mixture model is adopted to construct energy functions with the above probability distribution, and the hierarchical conditional random field is used to constraint these feature energy functions to refine final segmentation. The experiment results showed that the algorithm can avoid the error segmentation problem induced by camera movement. So it is robust to handle the videos under complex and dynamic scenes.