## 3 Results
### Comparison of the Initial Visual Model and the Late Fusion Model  
Following the testing phase of the meta-classifier with the late-fusion-merging technique, the evaluation metrics of the meta-classifier were compared, combining predictions from both visual and audio features with the performance of the visual model. These results are presented in Figure 14.  
The multimodal meta-classifier indicates stability, as shown in Figure 14, with performance metrics distributed across the spider diagram. Significant improvements in the recall, accuracy, F1-score, accuracy for aggression detections, and NPV are shown. However, minor reductions in the precision and PPV are observed, as well as a drop in the accuracy for non-aggression detections. These performance drops are because of the class imbalance in the late fusion merging. The drops in the performances of these three metrics are in the context of a meta-classifier model with balanced metrics.  
As shown in Figure 15, the initial visual model has shorter inference times (3.40 ms) than the meta-classifier's inference time (95.50 ms). The meta-classifier model is 28 times slower. The meta-classifier has to process both audio and visual features, increasing the computational complexity. A tradeoff can be observed in the metrics, where the meta-classifier outperforms the visual-only model in all the scores. This improved performance highlights that the new model significantly enhances the quality of the classification by incorporating audio features. Although speed is the key in real-world applications, the tradeoff for quality improvement is admissible.  
The statistical testing of results is shown in Figure 16. The \(t\)- and \(p\)-values were used to measure the difference in significance. A t-value indicates the size of the difference relative to the variability between the two models. For a significant difference, there must  
Figure 14: Performance comparison between the initial model, which uses visual cues only to predict aggressive behaviors, and the late fusion meta-classifier model, which utilizes both visual and audio features for predictions.  
be a very large absolute value. A \(p\)-value below 0.05 is the threshold for a significant statistical difference.  
Figure 16: Comparison of \(t\)-test statistical significances between the initial model, which uses visual cues only to predict aggressive behaviors, and the late fusion meta-classifier model, which utilizes both visual and audio features.  
Figure 15: Performance comparison of inference times between the initial model, which uses visual cues only, and the late fusion meta-classifier model, which utilizes both visual and audio features for the prediction of aggressive behaviors.  
* **Accuracy (Model 1: \(t=-1.5000\), \(p=0.2080\); Model 2: \(t=-1.2454\), \(p=0.2809\))** Both models have high \(p\)-values, significantly higher than the threshold value (0.05), indicating that there is no significant difference between the two models. Model 1 has a negative t-value and lower \(p\)-value, which point toward higher significance than the Model 2 comparison's threshold.
* **Precision (Model 1: \(t=-1.5042\), \(p=0.2070\); Model 2: \(t=-0.4129\), \(p=0.7008\))** Model 1 has a higher negative t-value and lower \(p\)-value, which demonstrates a higher significance than the Model 2 comparison's threshold. Model 2 exhibits the minimal precision difference, with a high \(p\)-value of 0.7008.
* **F1-Score (Model 1: \(t=-1.5021\), \(p=0.2075\); Model 2: \(t=-1.7476\), \(p=0.1555\))** Model 2 exhibits a higher negative t-value and a low \(p\)-value, which translates to no statistical significance, indicating that Model 2 is slightly better in the F1-score than Model 1.  
According to these findings, Model 1 has better precision, and Model 2 has a balanced performance with a higher F1-score. No significant statistical difference was observed between the two models.