Multi-scale anomaly behavior detection method based on Mamba-CNN

A U-shaped network is suggested for anomaly behavior recognition based on Mamba in order to overcome difficulties in unsupervised anomaly behavior detection, such as the predictor’s propensity for abnormal generalization and the target objects' scale disparities. The network improves both global and local features to constrain undesired generalization ability in predictions. A state space model is introduced in the encoder to strengthen the extraction of global features. A multi-scale spatial channel fusion (M-SCF) strategy is designed to integrate feature information from different receptive fields, thereby reducing the interference of scale differences on local features. Skip connections are used in the decoder to enrich shallow feature information and enhance the ability to capture contextual information. The proposed method has been extensively validated on the UCSD Ped2, Avenue, and Shanghai Tech datasets, with respective recognition accuracies of 98.1%, 89.8%, and 78.5%. Mamba can successfully increase the accuracy of abnormal behavior identification, as evidenced by the findings, which demonstrate superior accuracy when compared to several sophisticated algorithms in recent years.