Human Behavior Feature Representation and Recognition Based on Depth Video

Authors

  • Miao He School of Instrument Science and Engineering, Southeast University, Nanjing, 210018, China
  • Guangming Song School of Instrument Science and Engineering, Southeast University, Nanjing, 210018, China
  • Zhong Wei School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China

DOI:

https://doi.org/10.13052/jwe1540-9589.195614

Keywords:

Artificial Intelligence, Composite Features of Video Images, Motion Recognition, Feature Extraction Algorithm

Abstract

With the continuous development of computer artificial intelligence technology, various applications based on artificial intelligence emerge in an endless stream, among which video image recognition technology is the most widely used in life. This article starts from the process of image recognition, based on the composite characteristics of artificial intelligence and video images, to discuss human gesture recognition technology.This article uses the feature extraction algorithm for image composite feature extraction as a method, and conducts human body movement collection experiments, analyzes the database and The gesture recognition step. This paper mainly introduces the extraction method of image composite features and the basic requirements of gesture recognition, and through the algorithm calculation of feature extraction, the function of human gesture recognition video and image composite features is completed, and the human action collection experiment is carried out to confirm. The results of images and data show the advantages of the algorithm support used in this article. We will Dmti. MsHOG is compared with other methods in the three subsets. In terms of the accuracy of all tests, our method performs better than other methods. The results show that the MSHOG (Multi-scale Histogram of Oriented Gradients) descriptor can represent the unique characteristics of human behavior, reflecting the effectiveness of our proposed method. In particular, this method achieved 100% recognition accuracy in Test, with an average recognition accuracy of 94.91%, which is significantly better than existing methods.

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Author Biographies

Miao He, School of Instrument Science and Engineering, Southeast University, Nanjing, 210018, China

Miao He received the bacherlor’s degree from Northeast Petroleum University, China, in 2018. Now she studies in the School of Instrumen Science and Engineering at Southeast University. Her research interests include human-robot interaction and computer vision.

Guangming Song, School of Instrument Science and Engineering, Southeast University, Nanjing, 210018, China

Guangming Song received the Ph.D. degree in control science and engineering from the University of Science and Technology of China, Hefei, China, in 2004. From 2004 to 2006, He was a Research Fellow with the Robotic Sensor and Control Laboratory, Southeast University, China. Since 2006, he has been with the School of Instrument Science and Engineering, Southeast University, China. He is currently a Professor with the School of Instrument Science and Engineering, Southeast University, China. His current research interests include distributed robots, aerial manipulators, and bio-inspired legged robots. He won the second prize of the National Technology Invention Award of China in 2017.

Zhong Wei, School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China

Zhong Wei received the Ph.D. degree in instrumentation science and technology from the Southeast University, Nanjing, China, in 2019. He is currently a lecturer with the School of Automation, Nanjing University of Information Science and Technology, China. His current research interests include leg-wheel robots and bio-inspired legged robots.

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Published

2020-12-14

Issue

Section

Articles