Salient Object Detection in Video Based on Dynamic Attention Center

Salient object detection in video has attracted enormous research efforts for its wide applicability. But there are still some issues in restraining the disturbance of background, which make it difficult to detect salient object in complex scenarios. Inspired by the hypothesis of center prior in image domain, we novelly introduced the concept of dynamic attention center in video. The distance between specific regions and this center is used as a weighting term to restrain the influence of background disturbance and obtain more accurate spatial and temporal saliency maps. Besides, we develop a dynamic fusion method to combine the temporal and spatial saliency map, leading to higher spatiotemporal consistency. The experiments on Freiburg-Berkeley Motion Segmentation Dataset show that our method outperforms several state-of-art methods on subjective visual perception and objective measurements.