## 3 Methods
### Detect_SEAM
#### 3.3.1 Separated and Enhancement Attention Module  
The Separated and Enhancement Attention Module (SEAM) is designed to enhance feature representation, particularly for accurately detecting driver behavior by compensating for occlusion-induced feature loss. At the core of SEAM is the Channel and Spatial Mixing Module (CSMM). The CSMM utilizes different patch configurations to capture multiscale contextual features. Within each CSMM, depthwise separable convolution (DW-conv) is employed to efficiently learn the correlation of spatial dimensions and channels. If \(X_{in}\) represents the input to a feature extraction block, a residual depthwise convolution operation can be formulated as:  
\[X^{\prime}_{res}=X_{in}+\mathcal{B}(\text{GELU}(\text{Conv}_{dw}(X_{in};W_{dw}))) \tag{8}\]  
Depthwise separable convolution operates on individual input channels to learn channel-specific spatial importance, which significantly reduces the parameter count compared to standard convolutions. However, a characteristic of processing channels independently in this manner is that it initially neglects inter-channel relationships. To address this limitation and strengthen inter-channel dependencies, SEAM integrates a cross-channel fusion mechanism. Specifically, the outputs of channel-wise spatial convolutions (from the depthwise component) are effectively combined using \(1\times 1\) pointwise convolutions. Subsequently, a two-layer fully connected network is employed to aggregate global cross-channel information. This layered approach strengthens inter-channel dependencies, enabling effective compensation for feature loss caused by occlusion through learned correlations between occluded and non-occluded regions. As illustrated in Figure 5, the overall SEAM architecture processes an initial feature tensor through three distinct CSMMs, each configured with divergent patch parameters. These processed signals are subsequently aggregated with the original features through element-wise summation. This aggregated representation then undergoes spatial averaging via global pooling. The condensed feature representation traverses a dual-layer neural network comprising two cascaded fully connected architectures, which collaboratively generate a channel-wise attention-weighting matrix. Finally, the module's output is obtained by performing element-wise multiplication between this attention-weighting matrix (appropriately broadcast) and the original input feature map. This architectural design, particularly the use of depthwise separable convolutions within the CSMMs, leads to substantial model compression. For instance, a conventional convolutional layer with identical input/output dimensions (\(c\) channels) and kernel width \(k\) requires a significant number of trainable parameters (on the order of \(c^{2}\cdot k^{2}\)). Through the decomposed implementation inherent in depthwise separable convolution, this complexity is strategically distributed into two main components: spatial feature extraction (the depthwise part, requiring parameters on the order of \(c\cdot k^{2}\)) and cross-channel information fusion via pointwise convolutions (requiring parameters on the order of \(c^{2}\), assuming consistent channel dimensions). This cumulative reduction yields significantly lower parametric demands, proving particularly advantageous for embedded deployment scenarios requiring instantaneous response in driver behavior detection. As illustrated in Figure 5, the initial feature tensor undergoes parallel processing through three distinct CSMM configured with divergent patch configurations (where patch parameters govern the padding strategies applied to the feature map) [39]. These processed signals are subsequently aggregated with the original features through element-wise summation before proceeding to spatial averaging via global pooling. The condensed feature representation then traverses a dual-layer neural network comprising two cascaded fully connected architectures, which collaboratively generate a channel-wise attention-weighting matrix.