Behavior recognition technology research and application analysis
Kaixuan Wei · Applied and Computational Engineering · 2023
This paper provides a comprehensive overview of the current state of behavior recognition technology research and its applications in computer vision. Firstly, it discusses the fundamental concepts and categorization methods employed in behavior recognition technology, such as multimodality, double-sided depth photos, bone key points, and RGB data. These techniques enable the recognition and analysis of various human behaviors with high accuracy and precision. Furthermore, this paper highlights the vast potential of behavior recognition technology in several fields, including safety and education. In safety settings, behavior recognition technology can assist managers in identifying abnormal behaviors and enhancing safety precautions. In educational settings, behavior recognition technology can help teachers gain insight into student learning levels and enhance their teaching efficiency. Additionally, this technology can be used to identify patterns of behaviors that might indicate a student is struggling or needs extra support. The paper concludes with a summary of the current state of behavior recognition research and suggests areas for further investigation. One potential area for future research is the development of more accurate and efficient recognition models. Additionally, exploring the ethical implications and privacy concerns of behavior recognition technology is also essential. Overall, this paper emphasizes the immense potential of behavior recognition technology in various fields and encourages further research to realize its full potential. By leveraging the power of computer vision, we can gain valuable insights into human behavior that could have far-reaching implications for safety, education, and other areas of our lives