Visual saliency, which predicts regions in the field of view that draw the
most visual attention, has attracted a lot of interest from researchers. It has
already been used in several vision tasks, e.g., image classification, object
detection, foreground segmentation. Recently, the spectrum analysis based
visual saliency approach has attracted a lot of interest due to its simplicity
and good performance, where the phase information of the image is used to
construct the saliency map. In this paper, we propose a new approach for
detecting spatiotemporal visual saliency based on the phase spectrum of the
videos, which is easy to implement and computationally efficient. With the
proposed algorithm, we also study how the spatiotemporal saliency can be used
in two important vision task, abnormality detection and spatiotemporal interest
point detection. The proposed algorithm is evaluated on several commonly used
datasets with comparison to the state-of-art methods from the literature. The
experiments demonstrate the effectiveness of the proposed approach to
spatiotemporal visual saliency detection and its application to the above
vision tasks

本研究基于频谱分析提出了一种新方法，可用于检测视频中的时间空间视觉显著性，具有高效和易于实现的特点；并且探讨了这种显著性如何应用于检测异常情况和兴趣点，实验结果证明了该方法在这些任务中的有效性。