Video-based salient object detection via spatio-temporal difference and coherence

Salient object detection aims to extract the attractive objects in images and videos. It can support various robotics tasks and multimedia applications, such as object detection, action recognition and scene analysis. However, efficient detection of salient objects in videos still faces many challenges as compared to that in still images. In this paper, we propose a novel video-based salient object detection method by exploring spatio-temporal characteristics of video content, i.e., spatial-temporal difference and spatial-temporal coherence. First, we initialize the saliency map for each keyframe by deriving spatial-temporal difference from color cue and motion cue. Next, we generate the saliency maps of other frames by propagating the saliency intra and inter frames with the constraint of spatio-temporal coherence. Finally, the saliency maps of both keyframes and non-keyframes are refined in the saliency propagation. In this way, we can detect salient objects in videos efficiently by exploring their spatio-temporal characteristics. We evaluate the proposed method on two public datasets, named SegTrackV2 and UVSD. The experimental results show that our method outperforms the state-of-the-art methods when taking account of both effectiveness and efficiency.