Dangerous Action Recognition for Spatial-Temporal Graph Convolutional Networks

Human behavior recognition is important to research in intelligent surveillance systems. For the criminal act of dangerous accident prevention and identification, dangerous human behavior of recognition is more than important. Aiming at the identification of dangerous human behavior, a human risk be...

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Bibliographic Details
Published in2022 IEEE 12th International Conference on Electronics Information and Emergency Communication (ICEIEC) pp. 216 - 219
Main Authors Yan, Zhao, Yongfeng, Qi, Xiaoxu, Pei
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.07.2022
Subjects
Accident prevention
Conferences
Convolution
Criminal act prevention
Dangerous behavior recognition
Feature extraction
Hazards
Public safety
Skeleton
Spacetime graph convolution network
Surveillance
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Summary:Human behavior recognition is important to research in intelligent surveillance systems. For the criminal act of dangerous accident prevention and identification, dangerous human behavior of recognition is more than important. Aiming at the identification of dangerous human behavior, a human risk behavior detection algorithm based on a time-space graph convolutional network is proposed. The algorithm first extracts the skeleton sequence of human behavior by monitoring the video in the system. Then based on the obtained skeleton features, combined with the time vector in the skeleton sequence, a spatial time map model is established to identify dangerous behaviors. Finally, the hazard level of the behavior is determined according to the setting of the corresponding threshold. Experimental results on the Kinetics and RGB+D datasets show that the algorithm can identify a variety of dangerous behaviors and can effectively identify dangerous behaviors in different scenarios.
ISBN:9781665407533
1665407530
ISSN:2377-844X
DOI:10.1109/ICEIEC54567.2022.9835097