Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal
  Sensors
João Luzio Alexandre Bernardino Plinio Moreno
The aim of this work is to establish how accurately a recent semantic-based
foveal active perception model is able to complete visual tasks that are
regularly performed by humans, namely, scene exploration and visual search.
This model exploits the ability of current object detectors to localize and
classify a large number of object classes and to update a semantic description
of a scene across multiple fixations. It has been used previously in scene
exploration tasks. In this paper, we revisit the model and extend its
application to visual search tasks. To illustrate the benefits of using
semantic information in scene exploration and visual search tasks, we compare
its performance against traditional saliency-based models. In the task of scene
exploration, the semantic-based method demonstrates superior performance
compared to the traditional saliency-based model in accurately representing the
semantic information present in the visual scene. In visual search experiments,
searching for instances of a target class in a visual field containing multiple
distractors shows superior performance compared to the saliency-driven model
and a random gaze selection algorithm. Our results demonstrate that semantic
information, from the top-down, influences visual exploration and search tasks
significantly, suggesting a potential area of research for integrating it with
traditional bottom-up cues.