A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910472888909824 |
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| author | Kontostathis, Ioannis Apostolidis, Evlampios Mezaris, Vasileios |
| author_facet | Kontostathis, Ioannis Apostolidis, Evlampios Mezaris, Vasileios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02991 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods Kontostathis, Ioannis Apostolidis, Evlampios Mezaris, Vasileios Computer Vision and Pattern Recognition Multimedia 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. |
| title | A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2406.02991 |