  Automatically generating a summary of sports video poses the challenge of
detecting interesting moments, or highlights, of a game. Traditional sports
video summarization methods leverage editing conventions of broadcast sports
video that facilitate the extraction of high-level semantics. However,
user-generated videos are not edited, and thus traditional methods are not
suitable to generate a summary. In order to solve this problem, this work
proposes a novel video summarization method that uses players' actions as a cue
to determine the highlights of the original video. A deep neural network-based
approach is used to extract two types of action-related features and to
classify video segments into interesting or uninteresting parts. The proposed
method can be applied to any sports in which games consist of a succession of
actions. Especially, this work considers the case of Kendo (Japanese fencing)
as an example of a sport to evaluate the proposed method. The method is trained
using Kendo videos with ground truth labels that indicate the video highlights.
The labels are provided by annotators possessing different experience with
respect to Kendo to demonstrate how the proposed method adapts to different
needs. The performance of the proposed method is compared with several
combinations of different features, and the results show that it outperforms
previous summarization methods.
