Dual-Branch Network for Video Anomaly Detection Based on Feature Fusion

Anomaly detection is a critical task in video surveillance, with significant applications in the management and prevention of criminal activities. Traditional convolutional neural networks often struggle with motion modeling and multi-scale feature fusion due to their localized field of view. To address these limitations, this work proposes a Dual-Branch Interactive Feature Fusion Network (DBIFF-Net). DBIFF-Net integrates a CNN branch and a swin transformer branch to extract multi-scale features. To optimize these features for efficient fusion, an interactive fusion module is introduced to efficiently fuse these multi-scale features through skip connections. Then, the temporal shift module is employed to exploit dependencies between video frames, thereby improving the identification of anomalous events. Finally, the channel attention is utilized for decoder to better assist in restoring complex object features in the video. System performance is evaluated on three standard benchmark datasets. DBIFF-Net achieves the area under the receiver operating characteristic (AUC) of 97.7%, 84.5%, and 73.8% on the UCSD ped2, CUHK Avenue, and ShanghaiTech Campus dataset, respectively. Extensive experiments demonstrate that DBIFF-Net outperforms most state-of-the-art methods, validating the effectiveness of our method.