High-speed video salient object detection with temporal propagation using correlation filter

It is challenging for video salient object detection in the pursuit of high accuracy and fast speed with large amount of calculation in spatiotemporal domain. Most of existing methods use complex models with massive number of parameters to detect salient regions in video and cost a lot of time. In this paper, we propose a high-speed video salient object detection method at 0.5s each frame (including average 0.32 s for optical flow computation). It mainly consists of two modules, the initial spatiotemporal saliency module and the correlation filter based salient temporal propagation module. The former one integrates the spatial saliency by robust minimum barrier distance and boundary contrast cue with temporal saliency information from motion field. The latter one incorporates correlation filters to keep the saliency consistency between neighboring frames. The above two modules are finally fused together in an adaptive way. Comprehensive experiments on four benchmarks: SegTrack v1, SegTrack v2, FBMS and Visal dataset, clearly demonstrate that our algorithm shows better performance than the other state-of-art methods.