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Open AccessArticle
Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
by
Xiaochao Dang
Xiaochao Dang 1,2,
Kai Fan
Kai Fan 1,
Fenfang Li
Fenfang Li 1,*
,
Yangyang Tang
Yangyang Tang 1,
Yifei Gao
Yifei Gao 1 and
Yue Wang
Yue Wang 1
1
College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
2
Gansu Province Internet of Things Engineering Research Center, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(16), 7253; https://doi.org/10.3390/app14167253 (registering DOI)
Submission received: 12 July 2024
/
Revised: 13 August 2024
/
Accepted: 16 August 2024
/
Published: 17 August 2024
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Featured Application: This research has important applications in areas such as smart furniture and human-computer interaction. It will bring people a more efficient and comfortable living experience as well as a new smart experience.
Abstract
Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set.
Share and Cite
MDPI and ACS Style
Dang, X.; Fan, K.; Li, F.; Tang, Y.; Gao, Y.; Wang, Y.
Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud. Appl. Sci. 2024, 14, 7253.
https://doi.org/10.3390/app14167253
AMA Style
Dang X, Fan K, Li F, Tang Y, Gao Y, Wang Y.
Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud. Applied Sciences. 2024; 14(16):7253.
https://doi.org/10.3390/app14167253
Chicago/Turabian Style
Dang, Xiaochao, Kai Fan, Fenfang Li, Yangyang Tang, Yifei Gao, and Yue Wang.
2024. "Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud" Applied Sciences 14, no. 16: 7253.
https://doi.org/10.3390/app14167253
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