  In this work, we present an integrated system for spatiotemporal
summarization of 360-degrees videos. The video summary production mainly
involves the detection of salient events and their synopsis into a concise
summary. The analysis relies on state-of-the-art methods for saliency detection
in 360-degrees video (ATSal and SST-Sal) and video summarization (CA-SUM). It
also contains a mechanism that classifies a 360-degrees video based on the use
of static or moving camera during recording and decides which saliency
detection method will be used, as well as a 2D video production component that
is responsible to create a conventional 2D video containing the salient events
in the 360-degrees video. Quantitative evaluations using two datasets for
360-degrees video saliency detection (VR-EyeTracking, Sports-360) show the
accuracy and positive impact of the developed decision mechanism, and justify
our choice to use two different methods for detecting the salient events. A
qualitative analysis using content from these datasets, gives further insights
about the functionality of the decision mechanism, shows the pros and cons of
each used saliency detection method and demonstrates the advanced performance
of the trained summarization method against a more conventional approach.
