Wavelet-Based Time–Frequency Feature Fusion for Violence Detection

In the past few years, feature fusion-based violence detection has made remarkable progress. However, existing detection methods primarily focus on temporal feature analysis, which may result in an insufficient representation of the subtle variations inherent in violent behaviors, ultimately compromising detection performance. To overcome this limitation, this study introduces a Wavelet-Based Time–Frequency Feature Fusion (WTFF) method. Specifically, the Wavelet-Dilated Separable Convolution Module (WDCM) and the Time–Frequency Feature Fusion (TFFF) Network are designed. Firstly, the input video data is utilized by the WDCM to extract and process frequency-domain features, enabling the model to capture fine-grained behavioral details often overlooked in temporal analysis. Secondly, the TFFF fuses the temporal and frequency-domain features, thereby improving the model’s ability to discriminate violent events. Ultimately, the effectiveness and superiority of the proposed approach are demonstrated by experimental results on UCF-Crime, XD-Violence, and ShanghaiTech datasets, achieving 85.87% (AUC), 84.77% (AP), and 97.91% (AUC), respectively.