# DualCascadeTSF-MobileNetV2: A Lightweight Violence Behavior Recognition Model
## 1 Introduction  
Social safety and stability are the desired living conditions for every citizen. To prevent and reduce the occurrence of violent behavior, a large number of security personnel monitor social security conditions in real-time through surveillance cameras or frontline duty terminal devices. This monitoring method has significantly improved the response speed to violent behavior. However, relying solely on human supervision has two main issues: on the one hand, it is difficult for surveillance personnel to maintain high concentration at all times, leading to potential misjudments or missed detections of violent behavior; on the other hand, with the vast amount of surveillance data, it is difficult for personnel to quickly analyze complex situations and make effective judgments. Therefore, introducing intelligent monitoring systems to assist security personnel in detecting violent behavior is of great importance.  
The core technology of intelligent monitoring systems lies in the intelligent recognition of violent behavior, which is an important direction in the field of computer vision for behavior recognition. In recent years, deep learning-based behavior recognition methods have made significant progress, with many new models achieving increasingly precise results on public datasets. However, considering the practical needs of violent behavior recognition tasks, models need not only higher accuracy but also faster recognition speed to reduce latency, fewer parameters, and smaller model size for deployment on edge devices.  
In this context, we noticed the temporal shift module (TSM) designed by [PERSON] et al. [1]. This model interacts with the action information at different times by shifting the time steps of different channels in the temporal dimension. This allows the extraction of action features across three consecutive time steps with only a single 2D convolution, effectively achieving 3D convolutional performance with 2D convolutional efficiency without sacrificing accuracy. This significantly improves computational speed and reduces recognition latency.  
The design of TSM is very ingenious and still has potential for further exploration. For example, a longer-term information correlation can be achieved by fusing the features before and after the shift at the same time point; the number of parameters and the model volume can be further reduced by replacing the base model. Based on TSM and its subsequent research, this paper deeply explores its potential optimization space. The main contributions are as follows:  
1. Improved TSM based on previous studies and designed the Dual Cascade Temporal Shift and Fusion (DualCascadeTSF) module. This module cascades two temporal shift modules and enhances the model's ability to extract long-term temporal information by adding feature fusion [2] after each temporal shift. It improves the robustness and accuracy of the model without increasing additional parameters.
2. Combined the DualCascadeTSF module with the MobileNetV2. This not only improves the accuracy of MobileNetV2, but also makes it have fewer parameters and a faster operation speed compared to other classic models, making it more suitable for deployment on edge devices.