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

arXiv: https://arxiv.org/abs/1709.08421 · HTML: https://mlsanity.com/paper/1709.08421/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Video Summarization Techniques: A Comprehensive Review](https://mlsanity.com/paper/2410.04449/) (2024)
- [TrajSV: A Trajectory-based Model for Sports Video Representations and Applications](https://mlsanity.com/paper/2508.11569/) (2025)
- [AdSum: Two-stream Audio-visual Summarization for Automated Video Advertisement Clipping](https://mlsanity.com/paper/2510.26569/) (2025)

## Abstract (arXiv)

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.