# A Taxonomy and Dataset for 360{\deg} Videos

## TL;DR

This paper introduces a taxonomy for 360-degree videos, creates a dataset based on it, and analyzes viewer behavior and feedback to better understand how users engage with different video categories.

## Contribution

It presents a novel taxonomy for categorizing 360-degree videos and provides a new dataset with viewer traces and feedback for research purposes.

## Key findings

- Viewport patterns vary across categories
- Viewer feedback highlights experience differences
- Dataset includes 28 videos and traces from 60 participants

## Abstract

In this paper, we propose a taxonomy for 360{\deg} videos that categorizes videos based on moving objects and camera motion. We gathered and produced 28 videos based on the taxonomy, and recorded viewport traces from 60 participants watching the videos. In addition to the viewport traces, we provide the viewers' feedback on their experience watching the videos, and we also analyze viewport patterns on each category.