A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods
<|reference_start|>A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods: 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.<|reference_end|>
@article{kontostathis2024a,
    title={A Human-Annotated Video Dataset for Training and Evaluation of
  360-Degree Video Summarization Methods},
    author={Ioannis Kontostathis, Evlampios Apostolidis, Vasileios Mezaris},
    journal={arXiv preprint arXiv:2406.02991},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02991},
    primaryClass={cs.CV cs.MM}
}