A Self-Attention Augmented Graph Convolutional Clustering Networks for Skeleton-Based Video Anomaly Behavior Detection

In this paper, we propose a new method for detecting abnormal human behavior based on skeleton features using self-attention augment graph convolution. The skeleton data have been proved to be robust to the complex background, illumination changes, and dynamic camera scenes and are naturally constru...

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Hlavní autoři:	Chengming Liu, Ronghua Fu, Yinghao Li, Yufei Gao, Lei Shi, Weiwei Li
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2021-12-01
Edice:	Applied Sciences
Témata:	video anomaly detections skeleton self-attention graph convolutional networks
On-line přístup:	https://www.mdpi.com/2076-3417/12/1/4
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