# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 4 Experiments and Results
### Ablation Study
These categories, being smaller in magnitude, may not be as reliant on timing information as the ACTION module would suggest. This underscores the effectiveness of the CA module in augmenting the model's recognition of crucial spatial features in the current frame. It suggests that the CA module may be more proficient than the ACTION module in extracting non-temporal key features. Model 4, which integrates the CA and ACTION modules, demonstrates superior overall performance by leveraging  
Figure 15: Comparison of Model val_mAP. the enhancements from both branches. The Average Precision (AP) of each category surpasses that of Model 1, Model 2, and Model 3, resulting in a 7.08% improvement in mean Average Precision (mAP) compared to the original model. The combination of these two attention mechanisms reveals complementary effects for various behavioral categories. The CA module significantly enhances performance for categories abundant in channel features or requiring fine discrimination. Conversely, the ACTION module proves more crucial for categories emphasizing action coherence and temporal information. The integration of both mechanisms enables the model not only to possess robust recognition capabilities at a specific temporal instance but also to sustain this proficiency across time series, presenting a distinct advantage for target detection in dynamic scenarios. This implies that the synergy and complementarity of the two attentional mechanisms result in cumulative improvements in model performance. The divergent outcomes observed in each model underscore the distinct roles and advantages of the attentional mechanisms in feature extraction and their impact on enhancing the accuracy of specific types of behavior recognition. Based on these experimental findings, we can deduce that both the CA and ACTION attention modules prove effective in enhancing the SlowFast model, particularly when employed in conjunction. Table 4 serves as a model performance comparison. It consists of three columns, with the first column indicating the model names, the second column displaying the total number of model parameters (millions), and the third column showing the floating-point operations, i.e., FLOPs (G). The SlowFast model has approximately 33.646 million parameters, with FLOPs of around 40.82 G. The ACTION-SlowFast model, incorporating the ACTION attention module, has nearly the same number of parameters as the baseline model, but its FLOPs are slightly higher, at 40.87 G. The CA-SlowFast model, incorporating the CA attention module, has a parameter count of 59.46 million, indicating it is more complex than the baseline model, with FLOPs also slightly higher, at 41.48 G. The final CA-ACTION-SlowFast model contains approximately 59.456 million parameters, with total FLOPs of about 41.52 G. Although the CA-ACTION-SlowFast model has more parameters, its FLOPs increase slightly compared to the original SlowFast model, with the increase not being significant. This may be attributed to the increased computational effort during feature extraction with the introduction of the CA and ACTION modules. However, due to some degree of optimization within these modules, the overall increase in FLOPs is not substantial.