Video Salient Object Detection via Multiple Time-scale Analysis

This paper focuses on salient object detection in video by multiple time-scale analysis, which exploits the temporally consistent information under three different scales. In the first time-scale, we define an effective measure called motion contrast from both low-level cues and the optical flow fields. In the second time-scale, we propose a novel approach to repair the inaccurate motion contrast due to the mistake of optical flow. In the third time-scale, considering the low-contrast objects that stop moving for a certain amount of time and cannot remain prominent, we present a robust motion detection method based on point-tracking and trajectories clustering. Finally, the outcomes from the three time-scales jointly formulate the saliency detection by Bayesian inference. The proposed model is evaluated on the widely-used DAVIS and FBMS benchmark. Experiments demonstrate that our proposed model substantially outperforms the state-of-the-art saliency detection models.