Contrast Based Hierarchical Spatial-Temporal Saliency for Video

Predicting human attention for video is requires exploiting temporal knowledge included in the video. We propose a novel hierarchical spatial-temporal saliency model for video based on the centersurround framework using both static features and temporal features. Saliency cues are analyzed through a hierarchical segmentation model, and fused across multiple levels, yielding the spatial-temporal saliency map. An adaptive temporal window using motion information is also developed to combine saliency values of consecutive frames in order to keep temporal consistency across frames. Performance evaluation on several popular benchmark datasets validates that our method outperforms existing state-of-the-arts.