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

arXiv: https://arxiv.org/abs/2406.02991 · HTML: https://mlsanity.com/paper/2406.02991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1710.10755/) (2017)
- [A Memory Network Approach for Story-based Temporal Summarization of 360deg Videos](https://mlsanity.com/paper/1805.02838/) (2018)
- [Pano2Vid: Automatic Cinematography for Watching 360circ Videos](https://mlsanity.com/paper/1612.02335/) (2016)
- [Deep 360 Pilot: Learning a Deep Agent for Piloting through 360deg Sports Video](https://mlsanity.com/paper/1705.01759/) (2017)
- [ATSal: An Attention Based Architecture for Saliency Prediction in 360 Videos](https://mlsanity.com/paper/2011.10600/) (2020)
- [A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video](https://mlsanity.com/paper/1801.10312/) (2018)
- [Making 360circ Video Watchable in 2D: Learning Videography for Click Free Viewing](https://mlsanity.com/paper/1703.00495/) (2017)
- [An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos](https://mlsanity.com/paper/2312.02576/) (2023)

## Abstract (arXiv)

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.