A lightweight algorithm for video abnormal behavior detection by fusing wavelet transform and depthwise separable convolution

To address the challenges of high computational cost and parameter redundancy of video anomaly detection models in resource-constrained embedded systems, this paper proposes a lightweight algorithm that integrates wavelet transform and depth-separable convolution. Based on the GAFN baseline using an encoder-decoder architecture, we first reconstruct the standard convolution using a 3D depthwise separable convolutional attention module at encoder layers 3-4 and decoder layers 1-2. This decomposition method enhances the selectivity of spatial channel features. In addition, we introduce a 3D wavelet convolution at encoder layer 4, which utilizes the multi-scale nature of the wavelet transform to extend the receptive field and improve fine-grained feature extraction. Experiments demonstrate that our model achieves 95.0% and 83.5% AUC on UCSD Ped2 and CUHK Avenue datasets, respectively, and the number of covariates is only 1.0M, which is kept at 16.7% of the benchmark, indicating that the algorithm maintains high detection accuracy for video anomalous behaviors while the model is lightweight.