  The segmentation of a gaze trace into its constituent eye movements has been
actively researched since the early days of eye tracking. As we move towards
more naturalistic viewing conditions, the segmentation becomes even more
challenging and convoluted as more complex patterns emerge. The definitions and
the well-established methods that were developed for monitor-based eye tracking
experiments are often not directly applicable to unrestrained set-ups such as
eye tracking in wearable contexts or with head-mounted displays. The main
contributions of this work to the eye movement research for 360-degree content
are threefold: First, we collect, partially annotate, and make publicly
available a new eye tracking data set, which consists of 13 participants
viewing 15 video clips that are recorded in 360-degree. Second, we propose a
new two-stage pipeline for ground truth annotation of the traditional
fixations, saccades, smooth pursuits, as well as (optokinetic) nystagmus,
vestibulo-ocular reflex, and pursuit of moving objects performed exclusively
via the movement of the head. A flexible user interface for this pipeline is
implemented and made freely accessible for use or modification. Lastly, we
develop and test a simple proof-of-concept algorithm for automatic
classification of all the eye movement types in our data set based on their
operational definitions that were used for manual annotation. The data set and
the source code for both the annotation tool and the algorithm are publicly
available at https://web.gin.g-node.org/ioannis.agtzidis/360_em_dataset.
