期刊论文

Tu, Zhigang

英文

Pattern Recognition

0031-3203

2022

122

108213

10.1016/j.patcog.2021.108213

This work was supported by the National Natural Science Foundation of China under Grant 62106177. It was also supported by the Wuhan University-Infinova project No.2019010019. The numerical calculation was supported by the supercomputing system in the Super-computing Center of Wuhan University.

本校为其他机构

Anomaly detection in videos remains a challenging task due to the ambiguous definition of anomaly and the complexity of visual scenes from real video data. Different from the previous work which utilizes reconstruction or prediction as an auxiliary task to learn the temporal regularity, in this work, we explore a novel convolution autoencoder architecture that can dissociate the spatio-temporal representation to separately capture the spatial and the temporal information, since abnormal events are usually different from the normality in appearance and/or motion behavior. Specifically, the spat...