  With vast amounts of video content being uploaded to the Internet every
minute, video summarization becomes critical for efficient browsing, searching,
and indexing of visual content. Nonetheless, the spread of social and
egocentric cameras creates an abundance of sparse scenarios captured by several
devices, and ultimately required to be jointly summarized. In this paper, we
discuss the problem of summarizing videos recorded independently by several
dynamic cameras that intermittently share the field of view. We present a
robust framework that (a) identifies a diverse set of important events among
moving cameras that often are not capturing the same scene, and (b) selects the
most representative view(s) at each event to be included in a universal
summary. Due to the lack of an applicable alternative, we collected a new
multi-view egocentric dataset, Multi-Ego. Our dataset is recorded
simultaneously by three cameras, covering a wide variety of real-life
scenarios. The footage is annotated by multiple individuals under various
summarization configurations, with a consensus analysis ensuring a reliable
ground truth. We conduct extensive experiments on the compiled dataset in
addition to three other standard benchmarks that show the robustness and the
advantage of our approach in both supervised and unsupervised settings.
Additionally, we show that our approach learns collectively from data of varied
number-of-views and orthogonal to other summarization methods, deeming it
scalable and generic.
