Dynamic Gesture Recognition in Ultra-Range Distance for Effective Human-Robot Interaction

Eran Bamani Beeri, Eden Nissinman, Avishai Sintov
7/31/2024
cs.ROcs.CVcs.LG

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Abstract

This paper presents a novel approach for ultra-range gesture recognition, addressing Human-Robot Interaction (HRI) challenges over extended distances. By leveraging human gestures in video data, we propose the Temporal-Spatiotemporal Fusion Network (TSFN) model that surpasses the limitations of current methods, enabling robots to understand gestures from long distances. With applications in service robots, search and rescue operations, and drone-based interactions, our approach enhances HRI in expansive environments. Experimental validation demonstrates significant advancements in gesture recognition accuracy, particularly in prolonged gesture sequences.

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Cite this paper

@article{beeri2024dynamic,
  title  = {Dynamic Gesture Recognition in Ultra-Range Distance for Effective Human-Robot Interaction},
  author = {Eran Bamani Beeri and Eden Nissinman and Avishai Sintov},
  year   = {2024},
  eprint = {2407.21374},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.21374v1}
}

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