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Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media

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This study introduces a new method for interactive artistic installations (IAI) using Lightweight Deep Learning (LDL) and Virtual Vision Sensing Technology (VST). The approach synchronizes virtual and physical models for enhanced Human-Computer Interaction (HCI) and aesthetic experiences.

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Area of Science:

  • New Media Art
  • Human-Computer Interaction (HCI)
  • Computer Vision

Background:

  • Traditional interactive art devices and remote controls have limitations in meeting user needs.
  • Virtual Vision Sensing Technology (VST) offers potential for exploring emotional semantics in human-machine environments.
  • Lightweight Deep Learning (LDL) models enhance Interactive Artistic Installation (IAI) capabilities.

Purpose of the Study:

  • To optimize interactive art devices by integrating LDL and VST.
  • To analyze the impact of virtual VST on IAI's creative thinking, methods, and artistic experience.
  • To develop a scene construction method for real-time synchronization of virtual and physical models.

Main Methods:

  • Utilized Lightweight Deep Learning (LDL) models for Interactive Artistic Installation (IAI).
  • Implemented Virtual Vision Sensing Technology (VST) to analyze emotional semantics and promote Human-Computer Interaction (HCI).
  • Designed a scene construction method involving real-time loading of pre-modeled physical equipment and synchronization with visual information.

Main Results:

  • The proposed method achieves real-time loading of scene models and synchronization between virtual scenes and physical models.
  • Experimental results show that environmental factors significantly influence outcomes, with training data sets featuring boundary occlusion yielding better test results (approx. 97% accuracy).
  • The system does not require complex vision or laser scanning equipment or high-configured computer systems.

Conclusions:

  • The research enhances the performance of Virtual Reality works, particularly aesthetic experience.
  • This work enriches the theoretical foundation of new media art installation technology.
  • The developed method offers an efficient and accessible approach to creating synchronized interactive art experiences.