Motion-Aware Rapid Video Saliency Detection

In this paper, we propose a computationally efficient and consistently accurate spatiotemporal salient object detection method to identify the most noticeable object in a video sequence. Intuitively, the underlying motion in a video is a more stable saliency indicator than the apparent color cues that often contain significant variations and complex structures. Based on this observation, we build an efficient and accurate spatiotemporal saliency detection method that uses motion information as a leverage to locate the most dynamic regions in a video sequence. We first analyze the optical flow field to obtain foreground priors, and then incorporate spatial saliency features such as appearance contrasts and compactness measures, into a multi-cue integration framework to combine various saliency cues and achieve temporal consistency. Rigorous experiments on the challenging SegTrackV1, SegTrackV2, and FBMS datasets demonstrate that our method generates comparable or superior performance to state-of-the-art methods while running almost 100x faster at only 0.08 sec/frame. Promising performance and rapid speed imply that the proposed spatiotemporal saliency method can be easily involved in various vision applications.