Achieving real-time video summarization on commodity hardware
Date
2018-04-01
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Abstract
We present a system for automatic video summarization which is able to operate in
real-time on commodity hardware. This is achieved by performing segmentation
to divide a video into a series of small video clips, which are further reduced or
eliminated with the assistance of highly efficient low-level features. A numerical
score is then assigned to each segment by our model trained using a set of highperformance
hand-crafted features. Finally, segments are selected based on their
score to generate a final video summary. On our benchmark dataset, we achieve
results competitive to other methods. In cases where our accuracy is lower than
competitive methods, we achieve significantly higher performance. We additionally
present methods for generating additional summaries almost instantly, and for
learning user preferences over time—two processes which are often overlooked in
work on video summarization, but essential for real-world use
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Keywords
Video summarization, Machine learning, Computer vision