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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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A Passive Learning Sensor Architecture for Multimodal Image Labeling: An Application for Social Robots.

Marco A Gutiérrez1, Luis J Manso2, Harit Pandya3

  • 1Robotics and Artificial Vision Laboratory, University of Extremadura, 10003 Cáceres, Spain. marcog@unex.es.

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This study introduces a novel passive learning sensor architecture for social robots. It enhances object detection in low-resolution, variable lighting conditions, improving human-robot interaction.

Keywords:
ambient intelligence sensorsdeep learningobject detectionobject recognitionrobot sensorsword semantics

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Area of Science:

  • Robotics and Computer Vision
  • Human-Robot Interaction
  • Artificial Intelligence

Background:

  • Object detection and classification are crucial for autonomous robots in household tasks.
  • Real-world scenarios present challenges like environmental changes and low-quality sensor data.
  • Existing systems struggle with object detection in low-resolution and variable lighting.

Purpose of the Study:

  • To design a passive learning sensor architecture for improved object detection.
  • To leverage multimodal information from RGB-D cameras and semantic language models.
  • To enhance robot performance in challenging real-world conditions.

Main Methods:

  • Developed a passive learning architecture utilizing multimodal sensor data.
  • Integrated RGB-D camera input with trained semantic language models.
  • Employed a combination of image labeling and word semantics for sensor enhancement.

Main Results:

  • The proposed architecture significantly improves sensor performance under low resolution and high light variations.
  • Comparative tests show the architecture outperforms current state-of-the-art labeling techniques.
  • Demonstrated superior object detection capabilities for autonomous social robots in apartment environments.

Conclusions:

  • The novel sensor architecture effectively addresses limitations in low-quality sensor data.
  • This advancement is vital for successful ambient intelligence and human-robot interaction systems.
  • The approach offers a robust solution for object detection in complex, dynamic environments.