Summarization of User-Generated Sports Video by Using Deep Action Recognition Features

TL;DR

This paper introduces a deep learning-based method for summarizing user-generated sports videos by identifying highlights through action recognition, effectively handling unedited footage across various sports.

Contribution

It proposes a novel action-based video summarization approach using deep neural networks, adaptable to different sports and trained with diverse annotator labels.

Findings

Outperforms previous summarization methods

Effective in identifying highlights in unedited videos

Applicable to multiple sports including Kendo

Abstract

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.