Retrospective analysis of time series for frame selection in surveillance video summarization

In recent days, we have witnessed a dramatical growth of videos in various real-life scenarios. In this paper, we address the problem of surveillance video summarization. We present a new method of key-frame selection for this task: By virtue of retrospective analysis of time series, temporal cuts are first detected by sequentially measuring dissimilarities on a given video with threshold-based decision making; then, with the detected cuts, the video is segmented into a number of consecutive clips containing similar video contents; key frames are last selected by performing a typical clustering procedure in each resulted clip for final video summary. We have conducted extensive experiments on the benchmarking ViSOR dataset and the publicly available IVY LAB dataset. Excellent performances outperforming state-of-the-art competitors were demonstrated on key-frame selection for surveillance video summarization, which suggests good potentials of the proposed method in real-world applications.