  We present a method for creating video summaries in real-time on commodity
hardware. Real-time here refers to the fact that the time required for video
summarization is less than the duration of the input video. First, low-level
features are use to discard undesirable frames. Next, video is divided into
segments, and segment-level features are extracted for each segment. Tree-based
models trained on widely available video summarization and computational
aesthetics datasets are then used to rank individual segments, and top-ranked
segments are selected to generate the final video summary. We evaluate the
proposed method on SUMME dataset and show that our method is able to achieve
summarization accuracy that is comparable to that of a current state-of-the-art
deep learning method, while posting significantly faster run-times. Our method
on average is able to generate a video summary in time that is shorter than the
duration of the video.
