A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods
This provides a benchmark for researchers working on 360-degree video summarization, though it is incremental as it adapts existing methods to a new dataset.
The paper introduces a new human-annotated dataset for 360-degree video summarization, enabling the transformation of 360-degree content into concise 2D summaries, and uses it to train and evaluate two state-of-the-art methods as baselines.
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.