Experiment design for skeleton-based pedestrian abnormal-behavior recognition

Rongyong Zhao; Bingyu Wei; Wenjie Zhu; Haonan Li; Chengyuan Zheng

doi:10.1117/12.3011407

8 November 2023 Experiment design for skeleton-based pedestrian abnormal-behavior recognition

Rongyong Zhao, Bingyu Wei, Wenjie Zhu, Haonan Li, Chengyuan Zheng

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292305 (2023) https://doi.org/10.1117/12.3011407
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

The detection and recognition of pedestrian abnormal behavior have been currently attractive research hotspots in surveillance video content-analysis. However, due to the complexity and diversity of pedestrian abnormal behavior, the accurate and efficient behavior-recognition remains a challenging issue in the field of pedestrian safety supported by computer vision technologies. Previous studies mostly focused on detecting and locating abnormal behavior from given videos rather than recognizing pedestrian behavior, which may be incompetent in a few practical scenarios since the threat of each anomaly is needed to be evaluated. As individual pedestrian behaviors are closely related to human postures, in this study, we propose a novel experiment design approach for pedestrian abnormal-behavior recognition based on pose estimation and behavior recognition. We first classify and annotate the common abnormal behaviors in public crowds’ abnormal-behavior datasets, such as ShanghaiTech and CUHK Avenue. Then, the pose estimation toolbox, Openpose, is employed to extract skeleton sequences of behaviors annotated. Finally, the skeleton sequences are classified by the spatial-temporal graph convolutional network (ST-GCN) to implement behavior recognition. This proposed experiment design-approach can provide a technical support to validate the mathematic models or algorithms about pedestrian abnormal behavior recognition.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rongyong Zhao, Bingyu Wei, Wenjie Zhu, Haonan Li, and Chengyuan Zheng "Experiment design for skeleton-based pedestrian abnormal-behavior recognition", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292305 (8 November 2023); https://doi.org/10.1117/12.3011407

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Action recognition

Design and modelling

Pose estimation

Education and training

Video

Data modeling

Deep learning

Show All Keywords

Keywords/Phrases

Search In:

Publication Years