Research on the Method of Video Abstraction

Xueheng Wang

2025

Abstract

Video summarization aims to generate compact and informative representations of video content by extracting key frames or segments that best capture the original video’s semantics and narrative. With the explosive growth of video data, particularly in surveillance, sports, and entertainment, video summarization has become increasingly significant for efficient content browsing, indexing, and retrieval. Early summarization methods relied heavily on low-level visual features and clustering heuristics. However, with the advent of deep learning, especially recurrent neural networks and Transformer architectures, substantial improvements in summarization quality have been achieved. This review presents a structured analysis of recent advances in supervised and unsupervised video summarization techniques, focusing on attention mechanisms, multimodal fusion, etc. The paper also explores domain-specific applications, particularly in sports video summarization, where action localization and multimodal analysis have shown remarkable potential. A comparative analysis of evaluation metrics and benchmark datasets is included to provide insights into current challenges. Finally, the paper discusses open issues and propose future directions, such as improving temporal coherence, minimizing annotation costs, and integrating semantic understanding for enhanced summarization. This review aims to serve as a comprehensive reference for researchers and practitioners seeking to understand and advance the field of video summarization.

Paper Citation

in Harvard Style

Wang X. (2025). Research on the Method of Video Abstraction. In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-792-4, SciTePress, pages 438-446. DOI: 10.5220/0014360900004718

in Bibtex Style

@conference{emiti25,
author={Xueheng Wang},
title={Research on the Method of Video Abstraction},
booktitle={Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2025},
pages={438-446},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014360900004718},
isbn={978-989-758-792-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Research on the Method of Video Abstraction
SN - 978-989-758-792-4
AU - Wang X.
PY - 2025
SP - 438
EP - 446
DO - 10.5220/0014360900004718
PB - SciTePress