Video anomaly detection based on attention feature fusion
Currently,feature fusion methods based on attention mechanisms,such as multi-head self-attention,largely rely on the correlation between features,with limited cross-domain fusion capabilities.Additionally,due to existing domain differences among various features,the spatiotemporal perception capability of the fused features is insufficient.To address the insufficient cross-domain expression capability of RGB and optical flow features and the weak spatiotemporal perception capability of the fused features,a video anomaly detection method based on attentional feature fusion was proposed.Firstly,a lightweight attentional feature fusion module(LAFF)was employed to construct the fusion mechanism,combining RGB and optical flow features,enhancing the feature expression capabilities while reducing the network's parameter count and improving anomaly detection algorithm performance.Then,in the global spatiotemporal perception stage,a diverse branch block(DBB)was utilized to enhance the spatiotemporal perception capabilities of the features,while considering computational complexity and detection effectiveness.Finally,the proposed method achieved a recognition rate of 99.85%on the UCSD Ped2 dataset and demonstrated similarly strong performance on the CUHK Avenue and LAD 2000 datasets,validating the effectiveness of the approach.
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