A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods

Autor:	Kontostathis, Ioannis, Apostolidis, Evlampios, Mezaris, Vasileios
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Multimedia
Druh dokumentu:	Working Paper
Popis:	In this paper we introduce a new dataset for 360-degree video summarization: the transformation of 360-degree video content to concise 2D-video summaries that can be consumed via traditional devices, such as TV sets and smartphones. The dataset includes ground-truth human-generated summaries, that can be used for training and objectively evaluating 360-degree video summarization methods. Using this dataset, we train and assess two state-of-the-art summarization methods that were originally proposed for 2D-video summarization, to serve as a baseline for future comparisons with summarization methods that are specifically tailored to 360-degree video. Finally, we present an interactive tool that was developed to facilitate the data annotation process and can assist other annotation activities that rely on video fragment selection. Comment: Accepted for publication, 1st Int. Workshop on Video for Immersive Experiences (Video4IMX-2024) at ACM IMX 2024, Stockholm, Sweden, June 2024. This is the "accepted version"
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.02991 Zobrazit plný text záznamu View this record from Arxiv