## 2 Literature Review
### Behavior Recognition  
With the development of hardware resources in recent years, behavior recognition has become a hot topic. Many methods have added increasingly deeper network layers to improve recognition efficacy, which has led to an explosion in the number of parameters involved. However, using large models to enhance recognition invariably results in a higher threshold for model usage. Achieving a balance between accuracy and model size is crucial in many application scenarios.  
Convolutional neural network (CNN)-based behavior recognition algorithms are typically divided into two types, namely 2D CNN and 3D CNN, depending on whether they employ convolution in the time dimension. While 3D CNN-based methods can achieve promising performance, they are computationally intensive. To address this issue, several studies have aimed to reduce the computational complexity and simplify the structure to enhance the real-time performance of 3D convolutions, with notable examples including C3D [14], I3D [15], P3D [16], and R (2 + 1) D [17]. Although these methods simplify the structure and enhance the performance over typical 3D convolutions, they still struggle with real-time processing.  
While 2D convolution is less effective than 3D convolution at capturing temporal features, it requires significantly fewer parameters and computational resources. This makes 2D convolution more suitable for real-time applications [18]. Many pieces of research have proposed methods to extract features similar to those achieved with 3D convolution using 2D convolution. For instance, [PERSON] et al. [19] introduced the Temporal Segment Network (TSN), a video classification algorithm based on a 2D CNN. TSN employs a sparse temporal sample strategy and video-level supervision to effectively learn from entire action videos. It consists of spatial stream ConvNets and temporal stream ConvNets that process short, sparsely sampled snippets from the video sequence. Each stream generates an initial action class prediction, and the final video-level prediction is formed by aggregating predictions from these snippets. However, this method does not adequately capture the temporal relationships between snippets, which limits its ability to accurately recognize events defined by precise timing cues.  
[PERSON] et al. [20] proposed TSM, a generic and effective module, with high efficiency and performance. TSM shifts part of the channels along the temporal dimension on the feature map, thereby increasing information exchange among neighboring frames. It can be integrated into a 2D CNN to achieve temporal modelling, without the need for additional computation or parameters. TSM performs well in scenarios with clear boundaries. Inthe context of sharing internal video footage from self-driving cars, [PERSON] et al. [21] proposed a fusion surveillance solution for identifying violent and other unruly behaviors of car occupants, using algorithms such as SlowFast, TSN, and TSM to achieve excellent recognition and real-time performance. [PERSON] et al. [22] suggested a behavior recognition model based on SlowFast and the EPSA attention mechanism to address the issues arising from complex lighting conditions. However, the effectiveness of these methods is significantly hindered in environments with dim lighting and unclear boundaries.