Spatiotemporal salient object detection based on distance transform and energy optimization

In this paper, we present a novel spatiotemporal salient object detection method to produce high-quality saliency maps. The gradient of optical flow is adopted to coarsely locate the boundaries of salient object and the gray-weighted distance transform is adopted to highlight the whole salient object for a temporal saliency map. Furthermore, a confidence-guided energy function is proposed to adaptively fuse spatial and temporal saliency maps. Based on these efforts, our method can achieve good performance for complex scenes such as cluster backgrounds and non-rigid deformation. Experimental results on two benchmark datasets demonstrate the efficiency of the proposed saliency method.