  This paper proposes an efficient video summarization framework that will give
a gist of the entire video in a few key-frames or video skims. Existing video
summarization frameworks are based on algorithms that utilize computer vision
low-level feature extraction or high-level domain level extraction. However,
being the ultimate user of the summarized video, humans remain the most
neglected aspect. Therefore, the proposed paper considers human's role in
summarization and introduces human visual attention-based summarization
techniques. To understand human attention behavior, we have designed and
performed experiments with human participants using electroencephalogram (EEG)
and eye-tracking technology. The EEG and eye-tracking data obtained from the
experimentation are processed simultaneously and used to segment frames
containing useful information from a considerable video volume. Thus, the frame
segmentation primarily relies on the cognitive judgments of human beings. Using
our approach, a video is summarized by 96.5% while maintaining higher precision
and high recall factors. The comparison with the state-of-the-art techniques
demonstrates that the proposed approach yields ceiling-level performance with
reduced computational cost in summarising the videos.
