A deep ranking model for spatio-temporal highlight detection from a 360 ^◦ video

We address the problem of highlight detection from a 360 ^◦ video by summarizing it both spatially and temporally. Given a long 360 ^◦ video, we spatially select pleasantly-looking normal field-of-view (NFOV) segments from unlimited field of views (FOV) of the 360 ^◦ video, and temporally summarize it into a concise and informative highlight as a selected subset of subshots. We propose a novel deep ranking model named as Composition View Score (CVS) model, which produces a spherical score map of composition per video segment, and determines which view is suitable for highlight via a sliding window kernel at inference. To evaluate the proposed framework, we perform experiments on the Pano2Vid benchmark dataset (Su, Jayaraman, and Grauman 2016) and our newly collected 360 ^◦ video highlight dataset from YouTube and Vimeo. Through evaluation using both quantitative summarization metrics and user studies via Amazon Mechanical Turk, we demonstrate that our approach outperforms several state-of-the-art highlight detection methods. We also show that our model is 16 times faster at inference than AutoCam (Su, Jayaraman, and Grauman 2016), which is one of the first summarization algorithms of 360 ^◦ videos.