Stereoscopic video saliency detection based on spatiotemporal correlation and depth confidence optimization

Effective stereoscopic video saliency detection is challenging due to the diversity of visual contrast and the variability of object motion. In this paper, we propose a novel stereoscopic video saliency detection method based on spatiotemporal correlation and depth confidence optimization. First, spatial saliency is obtained by color contrast computation and further enhancing spatial correlation between neighbors. Second, temporal saliency is calculated by propagating motion information between frames. The special propagation in sequential frames and reverse sequential frames augments the temporal correlation. Furthermore, a depth confidence optimization is proposed to fuse the spatial saliency and temporal saliency to generate optimal saliency map adaptively. Experimental results on three datasets demonstrate the efficiency of the proposed saliency method.