Research on Human-Computer Interaction Behavior and Gesture Recognition Based on Machine Vision

Yiyuan Zhang

2025

Abstract

At present, significant breakthroughs have been made in human-computer interaction behavior and gesture recognition technology based on visual perception, which has shown important application value in the fields of rehabilitation medicine, intelligent furniture and virtual reality systems by capturing human movement characteristics. In chronological order, this study deeply analyzes the design mechanism and performance boundaries of typical algorithms at different stages of development. The detection framework of Histogram (HOG) with Support Vector Machine (SVM) as the core of manual feature engineering of early vision methods was introduced. The introduction of multimodal data fusion strategies in the mid-stage development includes the co-architecture of RGB-D sensors and inertial measurement units (IMUs), as well as modern deep learning methods that break through the limitations of traditional paradigms and include method models representing end-to-end networks such as Visual Background Extractor (VIBE) and Multimodal Fusion (MMF). At the same time, the performance of different vision method models on the dataset is compared, and the future trend and development of the current model are discussed.

Paper Citation

in Harvard Style

Zhang Y. (2025). Research on Human-Computer Interaction Behavior and Gesture Recognition Based on Machine Vision. In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-792-4, SciTePress, pages 55-60. DOI: 10.5220/0014318400004718

in Bibtex Style

@conference{emiti25,
author={Yiyuan Zhang},
title={Research on Human-Computer Interaction Behavior and Gesture Recognition Based on Machine Vision},
booktitle={Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2025},
pages={55-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014318400004718},
isbn={978-989-758-792-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Research on Human-Computer Interaction Behavior and Gesture Recognition Based on Machine Vision
SN - 978-989-758-792-4
AU - Zhang Y.
PY - 2025
SP - 55
EP - 60
DO - 10.5220/0014318400004718
PB - SciTePress