  Understanding human behavior is key for robots and intelligent systems that
share a space with people. Accordingly, research that enables such systems to
perceive, track, learn and predict human behavior as well as to plan and
interact with humans has received increasing attention over the last years. The
availability of large human motion datasets that contain relevant levels of
difficulty is fundamental to this research. Existing datasets are often limited
in terms of information content, annotation quality or variability of human
behavior. In this paper, we present TH\"OR, a new dataset with human motion
trajectory and eye gaze data collected in an indoor environment with accurate
ground truth for position, head orientation, gaze direction, social grouping,
obstacles map and goal coordinates. TH\"OR also contains sensor data collected
by a 3D lidar and involves a mobile robot navigating the space. We propose a
set of metrics to quantitatively analyze motion trajectory datasets such as the
average tracking duration, ground truth noise, curvature and speed variation of
the trajectories. In comparison to prior art, our dataset has a larger variety
in human motion behavior, is less noisy, and contains annotations at higher
frequencies.
