Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles

Yuzhu Jiang; Ying Li; Chao Yang; Weida Wang; Bin Xu

doi:10.1109/CVCI59596.2023.10397362

Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles

Yuzhu Jiang
, Ying Li
, Chao Yang
, Weida Wang
, Bin Xu

School of Mechanical Engineering

Beijing Institute of Technology

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

Abstract

Pedestrian pose estimation can provide a basis for planning and control systems of campus unmanned delivery vehicles to ensure pedestrian safety and improve delivery efficiency. A lightweight pedestrian pose estimation model is built in this paper, and a model training method incorporating campus pedestrian information is proposed. In order to verify the effectiveness of the proposed method and the real-time performance of the model, a comparative experiment is conducted and the real-time performance is tested on the server side and the AGV platform. The results show that the proposed model and training method can efficiently and accurately accomplish campus pedestrian pose estimation in real scene.

Original language	English
Title of host publication	Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350340488
DOIs	https://doi.org/10.1109/CVCI59596.2023.10397362
Publication status	Published - 2023
Event	7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023 - Changsha, China Duration: 27 Oct 2023 → 29 Oct 2023

Publication series

Name	Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023

Conference

Conference	7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023
Country/Territory	China
City	Changsha
Period	27/10/23 → 29/10/23

Keywords

Campus Unmanned Delivery Vehicles
Pedestrian Pose Estimation
Real-time Performance

Access to Document

10.1109/CVCI59596.2023.10397362

Cite this

Jiang, Y., Li, Y., Yang, C., Wang, W., & Xu, B. (2023). Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles. In Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023 (Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CVCI59596.2023.10397362

Jiang, Yuzhu ; Li, Ying ; Yang, Chao et al. / Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles. Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023).

@inproceedings{2a5ccfe0512d473fa040a095e6208b91,

title = "Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles",

abstract = "Pedestrian pose estimation can provide a basis for planning and control systems of campus unmanned delivery vehicles to ensure pedestrian safety and improve delivery efficiency. A lightweight pedestrian pose estimation model is built in this paper, and a model training method incorporating campus pedestrian information is proposed. In order to verify the effectiveness of the proposed method and the real-time performance of the model, a comparative experiment is conducted and the real-time performance is tested on the server side and the AGV platform. The results show that the proposed model and training method can efficiently and accurately accomplish campus pedestrian pose estimation in real scene.",

keywords = "Campus Unmanned Delivery Vehicles, Pedestrian Pose Estimation, Real-time Performance",

author = "Yuzhu Jiang and Ying Li and Chao Yang and Weida Wang and Bin Xu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023 ; Conference date: 27-10-2023 Through 29-10-2023",

year = "2023",

doi = "10.1109/CVCI59596.2023.10397362",

language = "English",

series = "Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023",

address = "United States",

}

Jiang, Y, Li, Y , Yang, C , Wang, W & Xu, B 2023, Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles. in Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023. Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023, Institute of Electrical and Electronics Engineers Inc., 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023, Changsha, China, 27/10/23. https://doi.org/10.1109/CVCI59596.2023.10397362

Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles. / Jiang, Yuzhu; Li, Ying ; Yang, Chao et al.
Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023).

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

TY - GEN

T1 - Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles

AU - Jiang, Yuzhu

AU - Li, Ying

AU - Yang, Chao

AU - Wang, Weida

AU - Xu, Bin

PY - 2023

Y1 - 2023

N2 - Pedestrian pose estimation can provide a basis for planning and control systems of campus unmanned delivery vehicles to ensure pedestrian safety and improve delivery efficiency. A lightweight pedestrian pose estimation model is built in this paper, and a model training method incorporating campus pedestrian information is proposed. In order to verify the effectiveness of the proposed method and the real-time performance of the model, a comparative experiment is conducted and the real-time performance is tested on the server side and the AGV platform. The results show that the proposed model and training method can efficiently and accurately accomplish campus pedestrian pose estimation in real scene.

AB - Pedestrian pose estimation can provide a basis for planning and control systems of campus unmanned delivery vehicles to ensure pedestrian safety and improve delivery efficiency. A lightweight pedestrian pose estimation model is built in this paper, and a model training method incorporating campus pedestrian information is proposed. In order to verify the effectiveness of the proposed method and the real-time performance of the model, a comparative experiment is conducted and the real-time performance is tested on the server side and the AGV platform. The results show that the proposed model and training method can efficiently and accurately accomplish campus pedestrian pose estimation in real scene.

KW - Campus Unmanned Delivery Vehicles

KW - Pedestrian Pose Estimation

KW - Real-time Performance

UR - https://www.scopus.com/pages/publications/85185375993

U2 - 10.1109/CVCI59596.2023.10397362

DO - 10.1109/CVCI59596.2023.10397362

M3 - Conference contribution

AN - SCOPUS:85185375993

T3 - Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023

BT - Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023

Y2 - 27 October 2023 through 29 October 2023

ER -

Jiang Y, Li Y , Yang C , Wang W , Xu B. Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles. In Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023). doi: 10.1109/CVCI59596.2023.10397362

Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles

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