  Alongside the prevalence of mobile videos, the general public leans towards
consuming vertical videos on hand-held devices. To revitalize the exposure of
horizontal contents, we hereby set forth the exploration of automated
horizontal-to-vertical (abbreviated as H2V) video conversion with our proposed
H2V framework, accompanied by an accurately annotated H2V-142K dataset.
Concretely, H2V framework integrates video shot boundary detection, subject
selection and multi-object tracking to facilitate the subject-preserving
conversion, wherein the key is subject selection. To achieve so, we propose a
Rank-SS module that detects human objects, then selects the subject-to-preserve
via exploiting location, appearance, and salient cues. Afterward, the framework
automatically crops the video around the subject to produce vertical contents
from horizontal sources. To build and evaluate our H2V framework, H2V-142K
dataset is densely annotated with subject bounding boxes for 125 videos with
132K frames and 9,500 video covers, upon which we demonstrate superior subject
selection performance comparing to traditional salient approaches, and exhibit
promising horizontal-to-vertical conversion performance overall. By publicizing
this dataset as well as our approach, we wish to pave the way for more valuable
endeavors on the horizontal-to-vertical video conversion task.
