融合小波变换和编解码注意力的异常检测.

Aiming at the problems of inaccurate prediction of normal video and poor ability of learning normal features in video anomaly detection, this paper proposed an anomaly detection model combining wavelet transform and encoder-decoder attention. The model introduced multi-level discrete wavelet transform, and designed a module of discrete wavelet transform fusion. The module concatenated the sub-bands obtained by decomposing video frames, and fed the result into depthwise separable convolution, and then fused with the encoder features to compensate for the high-frequency details lost in the down sampling process. The model also constructed encoder-decoder attention module. After performing difference of Gaussian operation on the encoder feature map, the attention weights were obtained along the horizontal and vertical directions respectively. And then the encoder features were aggregated according to the weights. Finally, the decoder features were associated to enhance the network’s learning of normal events. Experiments on Ped1,Ped2 and Avenue datasets show that the AUC of the proposed model is increased by 3.2%,3.1% and 2.0%.And the results indicate that the proposed model can effectively improve the abnormal detection ability. [ABSTRACT FROM AUTHOR]