## 4 Experiments
### Results
#### 4.3.3 Results of Behavior Recognition  
In regard to the behavior recognition module, a comparative analysis is conducted among the classical TSM, the improved TSM, and the popular SlowFast algorithm, using the SCA dataset. The comparison is based on three dimensions: GMAC, the number of parameters, and Top-1 accuracy. As can be observed from Table 6, the improved TSM algorithm demonstrates superior performance compared to both the original TSM model and the SlowFast model. To further validate the effectiveness of the proposed improved TSM method, ablation experiments are conducted, and the results are presented in Table 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Backbone** & **GMAC** & **Parameters/M** & **Top-1 Accuracy/\%** \\ \hline \multirow{8}{*}{TSM} & ResNet50 & 132.17 & 23.52 & 77.79 \\  & ResNet101 & 251.62 & 42.51 & 81.4 \\  & EfficientNet-B0 & 13.07 & 4.02 & 76.34 \\  & EfficientNet-B1 & 19.26 & 6.52 & 80.65 \\  & EfficientNet-B2 & 22.16 & 7.71 & 78.50 \\  & EfficientNet-B3 & 32.23 & 10.71 & 80.11 \\  & EfficientNet-B4 & 50 & 17.56 & 77.42 \\ \hline \multirow{2}{*}{SlowFast} & ResNet50 & 101.16 & 33.66 & 75.1 \\  & ResNet101 & 163.88 & 52.87 & 76.66 \\ \hline Improved TSM & EfficientNet-B3 & 32.28 & 10.93 & 82.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different models. GMAC values represent billions of multiply–accumulate operations. Figure 5: Variation of metrics with parameter changes: (**a**) impact on metrics with the confidence threshold fixed at 0.2, while varying the IoU threshold; and (**b**) impact on metrics with the IoU threshold fixed at 0.2, while varying the confidence threshold. The baseline model M1 is based on the TSM architecture, with the EfficientNet-B3 backbone. M2 improves upon M1 by incorporating image augmentation, utilizing a super-resolution reconstructed dataset, and refining the pre-processing strategy. As a result, the recognition accuracy increases by 1.29 percentage points. Building on M2, M3 introduces the CA attention module into the feature extraction layers. With only a marginal increase in the computational cost, this modification effectively enhances the model's representational capability, achieving a Top-1 accuracy of 82.1%. To comprehensively evaluate the recognition performance of the improved TSM model, comparative experiments are conducted on two widely used benchmark datasets, UCF101 and HMDB51, which contain more diverse action categories than typical evaluation datasets. The Top-1 accuracy serves as the primary evaluation metric, with the detailed comparative results systematically presented in Table 8.  
The improved TSM is benchmarked against state-of-the-art approaches to action recognition, including the 2D baseline TSN, I3D, and Vision Transformer-based methods [38]. The experimental results demonstrate that the improved TSM model achieves a competitive Top-1 accuracy of 96.7% and 75.1% based on the UCF101 and HMDB51 datasets, respectively. While slightly lower than the ViT-based AMD method, it exhibits significant advantages in regard to its lightweight design, with substantially fewer parameters, namely 10.93 M, compared to Transformer-based architectures. The feature extraction capability is a critical determinant of a model's performance. By comparing the shallow feature maps of identical input samples across different model architectures, deeper insights into the initial stages of feature recognition can be gained, as illustrated in Figure 6.  
In regard to the TSM-ResNet series of models, the shallow feature maps exhibit a notable homogenization tendency. As shown in Figure 6b,c, the feature responses at different locations show a high level of similarity. This phenomenon indicates that the model fails to effectively capture the multidimensional characteristics of the input data in regard to the initial layers, thereby limiting the richness and expressiveness of subsequent deep features.