  In human-robot collaboration, one challenging task is to teach a robot new
yet unknown objects enabling it to interact with them. Thereby, gaze can
contain valuable information. We investigate if it is possible to detect
objects (object or no object) merely from gaze data and determine their
bounding box parameters. For this purpose, we explore different sizes of
temporal windows, which serve as a basis for the computation of heatmaps, i.e.,
the spatial distribution of the gaze data. Additionally, we analyze different
grid sizes of these heatmaps, and demonstrate the functionality in a proof of
concept using different machine learning techniques. Our method is
characterized by its speed and resource efficiency compared to conventional
object detectors. In order to generate the required data, we conducted a study
with five subjects who could move freely and thus, turn towards arbitrary
objects. This way, we chose a scenario for our data collection that is as
realistic as possible. Since the subjects move while facing objects, the
heatmaps also contain gaze data trajectories, complicating the detection and
parameter regression. We make our data set publicly available to the research
community for download.

Robotics;Human-Computer Interaction;Machine Learning