  This paper proposes a novel lightweight thumbnail container-based
summarization (LTC-SUM) framework for full feature-length videos. This
framework generates a personalized keyshot summary for concurrent users by
using the computational resource of the end-user device. State-of-the-art
methods that acquire and process entire video data to generate video summaries
are highly computationally intensive. In this regard, the proposed LTC-SUM
method uses lightweight thumbnails to handle the complex process of detecting
events. This significantly reduces computational complexity and improves
communication and storage efficiency by resolving computational and privacy
bottlenecks in resource-constrained end-user devices. These improvements were
achieved by designing a lightweight 2D CNN model to extract features from
thumbnails, which helped select and retrieve only a handful of specific
segments. Extensive quantitative experiments on a set of full 18 feature-length
videos (approximately 32.9 h in duration) showed that the proposed method is
significantly computationally efficient than state-of-the-art methods on the
same end-user device configurations. Joint qualitative assessments of the
results of 56 participants showed that participants gave higher ratings to the
summaries generated using the proposed method. To the best of our knowledge,
this is the first attempt in designing a fully client-driven personalized
keyshot video summarization framework using thumbnail containers for
feature-length videos.
