Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model

Xiulin Zhang; Chong Zhen; Quxiao Lei; Yifeng Wang; Jiaao Chen; Weiyi Jin; Ke Li; Lijing Wang

doi:10.1007/978-981-19-4786-5_40

Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model

Xiulin Zhang
, Chong Zhen
, Quxiao Lei
, Yifeng Wang
, Jiaao Chen
, Weiyi Jin
, Ke Li^*
, Lijing Wang

^*Corresponding author for this work

School of Aeronautic Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In the autonomous motion control of unmanned equipment, in order to make the control more precise and efficient, in addition to the automatic operation of drones and unmanned vehicles, it is also necessary to introduce a certain amount of human-computer interaction. Therefore, in the control of unmanned equipment, it is very important to design a human-computer interaction program. In this paper, by analyzing the control requirements of unmanned equipment in specific cases and comparing with existing technologies, a gesture recognition program is established. The results show that it is feasible to control the movement of unmanned equipment through gesture recognition technology. Moreover, gesture control has the advantages of high accuracy, simplicity and efficiency, and can control multiple unmanned equipment in real time.

Original language	English
Title of host publication	Man-Machine-Environment System Engineering - Proceedings of the 22nd International Conference on MMESE
Editors	Shengzhao Long, Balbir S. Dhillon
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	287-294
Number of pages	8
ISBN (Print)	9789811947858
DOIs	https://doi.org/10.1007/978-981-19-4786-5_40
State	Published - 2023
Event	22nd International Conference on Man-Machine-Environment System Engineering, MMESE 2022 - Beijing, China Duration: 21 Oct 2022 → 23 Oct 2022

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	941 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	22nd International Conference on Man-Machine-Environment System Engineering, MMESE 2022
Country/Territory	China
City	Beijing
Period	21/10/22 → 23/10/22

Keywords

Algorithm program
Gesture recognition technology
Unmanned equipment control
Verification on unmanned vehicle

Access to Document

10.1007/978-981-19-4786-5_40

Cite this

Zhang, X., Zhen, C., Lei, Q., Wang, Y., Chen, J., Jin, W., Li, K., & Wang, L. (2023). Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model. In S. Long, & B. S. Dhillon (Eds.), Man-Machine-Environment System Engineering - Proceedings of the 22nd International Conference on MMESE (pp. 287-294). (Lecture Notes in Electrical Engineering; Vol. 941 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-4786-5_40

Zhang, Xiulin ; Zhen, Chong ; Lei, Quxiao et al. / Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model. Man-Machine-Environment System Engineering - Proceedings of the 22nd International Conference on MMESE. editor / Shengzhao Long ; Balbir S. Dhillon. Springer Science and Business Media Deutschland GmbH, 2023. pp. 287-294 (Lecture Notes in Electrical Engineering).

@inproceedings{e53dc9aff58d4c06a719f5021c1acdd9,

title = "Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model",

abstract = "In the autonomous motion control of unmanned equipment, in order to make the control more precise and efficient, in addition to the automatic operation of drones and unmanned vehicles, it is also necessary to introduce a certain amount of human-computer interaction. Therefore, in the control of unmanned equipment, it is very important to design a human-computer interaction program. In this paper, by analyzing the control requirements of unmanned equipment in specific cases and comparing with existing technologies, a gesture recognition program is established. The results show that it is feasible to control the movement of unmanned equipment through gesture recognition technology. Moreover, gesture control has the advantages of high accuracy, simplicity and efficiency, and can control multiple unmanned equipment in real time.",

keywords = "Algorithm program, Gesture recognition technology, Unmanned equipment control, Verification on unmanned vehicle",

author = "Xiulin Zhang and Chong Zhen and Quxiao Lei and Yifeng Wang and Jiaao Chen and Weiyi Jin and Ke Li and Lijing Wang",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 22nd International Conference on Man-Machine-Environment System Engineering, MMESE 2022 ; Conference date: 21-10-2022 Through 23-10-2022",

year = "2023",

doi = "10.1007/978-981-19-4786-5\_40",

language = "英语",

isbn = "9789811947858",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "287--294",

editor = "Shengzhao Long and Dhillon, \{Balbir S.\}",

booktitle = "Man-Machine-Environment System Engineering - Proceedings of the 22nd International Conference on MMESE",

address = "德国",

}

Zhang, X, Zhen, C, Lei, Q, Wang, Y, Chen, J, Jin, W, Li, K & Wang, L 2023, Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model. in S Long & BS Dhillon (eds), Man-Machine-Environment System Engineering - Proceedings of the 22nd International Conference on MMESE. Lecture Notes in Electrical Engineering, vol. 941 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 287-294, 22nd International Conference on Man-Machine-Environment System Engineering, MMESE 2022, Beijing, China, 21/10/22. https://doi.org/10.1007/978-981-19-4786-5_40

Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model. / Zhang, Xiulin; Zhen, Chong; Lei, Quxiao et al.
Man-Machine-Environment System Engineering - Proceedings of the 22nd International Conference on MMESE. ed. / Shengzhao Long; Balbir S. Dhillon. Springer Science and Business Media Deutschland GmbH, 2023. p. 287-294 (Lecture Notes in Electrical Engineering; Vol. 941 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model

AU - Zhang, Xiulin

AU - Zhen, Chong

AU - Lei, Quxiao

AU - Wang, Yifeng

AU - Chen, Jiaao

AU - Jin, Weiyi

AU - Li, Ke

AU - Wang, Lijing

PY - 2023

Y1 - 2023

N2 - In the autonomous motion control of unmanned equipment, in order to make the control more precise and efficient, in addition to the automatic operation of drones and unmanned vehicles, it is also necessary to introduce a certain amount of human-computer interaction. Therefore, in the control of unmanned equipment, it is very important to design a human-computer interaction program. In this paper, by analyzing the control requirements of unmanned equipment in specific cases and comparing with existing technologies, a gesture recognition program is established. The results show that it is feasible to control the movement of unmanned equipment through gesture recognition technology. Moreover, gesture control has the advantages of high accuracy, simplicity and efficiency, and can control multiple unmanned equipment in real time.

AB - In the autonomous motion control of unmanned equipment, in order to make the control more precise and efficient, in addition to the automatic operation of drones and unmanned vehicles, it is also necessary to introduce a certain amount of human-computer interaction. Therefore, in the control of unmanned equipment, it is very important to design a human-computer interaction program. In this paper, by analyzing the control requirements of unmanned equipment in specific cases and comparing with existing technologies, a gesture recognition program is established. The results show that it is feasible to control the movement of unmanned equipment through gesture recognition technology. Moreover, gesture control has the advantages of high accuracy, simplicity and efficiency, and can control multiple unmanned equipment in real time.

KW - Algorithm program

KW - Gesture recognition technology

KW - Unmanned equipment control

KW - Verification on unmanned vehicle

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DO - 10.1007/978-981-19-4786-5_40

M3 - 会议稿件

AN - SCOPUS:85136933144

SN - 9789811947858

T3 - Lecture Notes in Electrical Engineering

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BT - Man-Machine-Environment System Engineering - Proceedings of the 22nd International Conference on MMESE

A2 - Long, Shengzhao

A2 - Dhillon, Balbir S.

PB - Springer Science and Business Media Deutschland GmbH

T2 - 22nd International Conference on Man-Machine-Environment System Engineering, MMESE 2022

Y2 - 21 October 2022 through 23 October 2022

ER -

Zhang X, Zhen C, Lei Q, Wang Y, Chen J, Jin W et al. Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model. In Long S, Dhillon BS, editors, Man-Machine-Environment System Engineering - Proceedings of the 22nd International Conference on MMESE. Springer Science and Business Media Deutschland GmbH. 2023. p. 287-294. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-19-4786-5_40

Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model

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Keywords

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