Institution:控制科学与工程学院

Title of Paper:A Self-trained Spatial Graph Convolutional Network for Unsupervised Human-related Anomalous Event Detection in Complex Scenes

Journal:IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS

Key Words:Anomaly detection;Event detection;Feature extraction;human skeleton;Human-related anomaly detection;self-training regression;Skeleton;spatial graph convolutional network;Task analysis;Training;Trajectory;video surveillance

First Author:李南君

Document Code:1559363562041430018

Number of Words:10

Translation or Not:No

Date of Publication:2022-01

Release Time:2023-06-05