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...

Deskribapen osoa

Xehetasun bibliografikoak

Egile Nagusiak:	Chengming Liu, Ronghua Fu, Yinghao Li, Yufei Gao, Lei Shi, Weiwei Li
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	MDPI AG 2021-12-01
Saila:	Applied Sciences
Gaiak:	video anomaly detections skeleton self-attention graph convolutional networks
Sarrera elektronikoa:	https://www.mdpi.com/2076-3417/12/1/4
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!