A video anomaly detection method with mask convolution and channel attention

Abstract

Aiming at the problem of Insufficient feature extraction for data sets in video anomaly detection, an enhanced detection method combining masked convolution and channel attention is proposed. The auto-encoder structure is adapted, and a mask convolution module and a channel attention module are embedded in the memory model. The effectiveness of the proposed method is demonstrated by comparing the results of the method and the original method.