Gaze-based Object Detection in the Wild
- URL: http://arxiv.org/abs/2203.15651v1
- Date: Tue, 29 Mar 2022 15:10:17 GMT
- Title: Gaze-based Object Detection in the Wild
- Authors: Daniel Weber, Wolfgang Fuhl, Andreas Zell, Enkelejda Kasneci
- Abstract summary: In human-robot collaboration, one challenging task is to teach a robot new yet unknown objects.
We investigate if it is possible to detect objects (object or no object) from gaze data and determine their bounding box parameters.
- Score: 23.923563888749108
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In human-robot collaboration, one challenging task is to teach a robot new
yet unknown objects. Thereby, gaze can contain valuable information. We
investigate if it is possible to detect objects (object or no object) 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 various machine learning
techniques are applied. To generate the data, we conducted a small 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.
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