Video anomaly detection algorithm combining mixed convolution and multi-scale attention
Unsupervised video anomaly detection model based on U-net style has good detection results,but due to the inherent local nature of ordinary convolutional operations use,the U-Net style encoder can not effectively extract the global contextual information,the use of simple jump connections can not obtain effective feature information,and the use of the L2 loss function only considers the pixel level differences and can not capture the image's structural features.In this regard,a video anomaly detection algorithm combining hybrid convolution and multi-scale attention is proposed,and a structural similarity loss function(SSIM)optimisation model is added.Specifically,a hybrid convolution module is added to the last layer of the encoder,which mixes spatial and positional features to extract global contextual information.A multiscale attention module is added to the hopping connection between the encoder and the decoder,which enables the model to extract more valuable features for effective hopping connection.The weights of the structural similarity loss function and the L2 loss function are constrained using parameters to optimise the model more accurately.Experimental results show that the proposed algorithm achieves AUC metrics of 96.7%and 86.1%on the UCSD-Ped2 and CUHK Avenue public datasets,which is an improvement of 1.6%and 1.4%compared with the pre-improvement model,proving the effectiveness of the proposed model.
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