The interaction between the vestibular and ocular system has primarily been
studied in controlled environments. Consequently, off-the shelf tools for
categorization of gaze events (e.g. fixations, pursuits, saccade) fail when
head movements are allowed. Our approach was to collect a novel, naturalistic,
and multimodal dataset of eye+head movements when subjects performed everyday
tasks while wearing a mobile eye tracker equipped with an inertial measurement
unit and a 3D stereo camera. This Gaze-in-the-Wild dataset (GW) includes
eye+head rotational velocities (deg/s), infrared eye images and scene imagery
(RGB+D). A portion was labelled by coders into gaze motion events with a mutual
agreement of 0.72 sample based Cohen's $\kappa$. This labelled data was used to
train and evaluate two machine learning algorithms, Random Forest and a
Recurrent Neural Network model, for gaze event classification. Assessment
involved the application of established and novel event based performance
metrics. Classifiers achieve $\sim$90$\%$ human performance in detecting
fixations and saccades but fall short (60$\%$) on detecting pursuit movements.
Moreover, pursuit classification is far worse in the absence of head movement
information. A subsequent analysis of feature significance in our
best-performing model revealed a reliance upon absolute eye and head velocity,
indicating that classification does not require spatial alignment of the head
and eye tracking coordinate systems. The GW dataset, trained classifiers and
evaluation metrics will be made publicly available with the intention of
facilitating growth in the emerging area of head-free gaze event
classification.

通过收集 GW 数据集（该数据集包括眼部运动、头部角速度、眼睛和场景图像），训练和评估了两种机器学习算法（随机森林和递归神经网络模型），用于眼睛运动分类（如注视、眼球、追踪）。分类器实现了近似于人类检测注视和眼球运动的 90％的性能，但在检测追踪运动时则低于 60％，并且追踪分类在没有头部运动信息的情况下更低。最佳模型对特征的显着性分析显示，分类不需要头部和眼睛跟踪坐标系的空间对齐。