# DualCascadeTSF-MobileNetV2: A Lightweight Violence Behavior Recognition Model
## 2 Related Work
### TSM and Two-Cascade TSM  
In behavior recognition tasks, different from static image input, the number of video stream parameters shows explosive growth, which poses a huge challenge to achieving high-accuracy and low-cost behavior recognition.  
Traditional 2D convolutional neural networks (CNNs) have a low computational cost but cannot capture the unique temporal information of videos; while 3D CNNs can achieve good performance, their computational load is huge, and the deployment cost is too high. To solve this problem, the temporal shift module (TSM) came into being, which cleverly achieved the goal of approaching the performance of 3D CNNs with the computational cost of 2D CNNs.  
Specifically, the working principle of TSM [1] is shown in Figure 1. Figure 1a represents the video stream before temporal shift, where \(C\) is the channel dimension, representing different channels of the video stream, and \(T\) is the temporal dimension, with each layer of color corresponding to a time step in the sequence. Figure 1b shows the video stream after temporal shifting, where some channels are shifted upward or downward by one time step in the temporal dimension, while the remaining parts stay unchanged. Through this approach, the task of extracting behavior features across any three consecutive time steps can be accomplished with a single convolution operation, meaning that 2D convolution alone can be used to extract spatio-temporal features for actions at a single time step.  
Therefore, the operation of TSM can be divided into two steps: temporal shifting and convolution. Suppose that there is a \(1\times 3\) convolution kernel \(W=(\omega_{1},\omega_{2},\omega_{3})\) and an infinitely long video stream \(X_{n\times 3}^{k}=(X_{i-k,i},X_{i,j},X_{i+k,j})\in\mathbb{R}^{n\times 3}\) with three channels, where \(k\) represents the number of temporal shifts. After one temporal shift, the video stream becomes \(X_{n\times 3}^{1}=(X_{i-1,j},X_{i2},X_{i+1,j})\in\mathbb{R}^{n\times 3}\), and then the convolution operation is performed:  
\[y_{i}=\omega_{1}x_{i-1,1}+\omega_{2}x_{i,2}+\omega_{3}x_{i+1,3} \tag{1}\]  
\[Y=\sum_{j=1}^{3}(\omega_{j}x_{i-1,j}) \tag{2}\]  
where \(y_{i}\) represents the convolution operation process, and \(Y\) represents the calculation result. From the calculation process, it can be seen that the first step of the TSM operation does not generate computational costs, while the second step will generate a certain amount of calculation. However, compared to the reduction in computational cost from transforming the convolution operation from 3D to 2D, the slight increase in computation is acceptable.  
Building on this foundation, [PERSON] et al. [15] further proposed the Two-cascade TSM. This module extends the original TSM by cascading another TSM within the residual block, enhancing the model's temporal correlation capability through the method of expanding the model's receptive field, thus enabling it to capture longer-term information dependencies. However, while this improvement enhances model performance, the authors discovered a problem: when cascading more TSMs, the model's performance deteriorates instead of improving. Analyzing the reason, this may be because too many temporal shift operations cause the feature maps that are originally adjacent in different channels to become sparse in the temporal dimension, thereby weakening the model's performance. Based on this drawback of the Two-cascade TSM, this paper conducts research on how to address the issue of sparse temporal information connections after long-term shifting.