## 4 Results and Discussion
### Full System Evaluation  
The performance of the full system in comparison to human expert responses is provided in Table 4 in terms of accuracy, precision, and recall. The complete system is evaluated on both the UTI and UOP datasets. However, it should be noted that the UTI dataset does not contain anyone wearing masks, and hence, the mask detection component does not contribute to the threat calculation here. It can be noted that the system performance is not biased toward either dataset and is able to generalize with considerable accuracy of nearly 76%.  
A few of the notable failure cases of the system are shown in Figures 14-17, where the threat level predicted was contrary to human expert opinion. Out of the four cases shown here, three of them failed to evaluate the proper threat value due to a failure in one of the components in the system pipeline. In Figure 14, the person indicated by the purple arrow was not detected by the person detection model due to occlusion. Similarly, in Figure 15, the two individuals hugging are detected as a single person. Since it is the proximity of the three individuals in Figure 14 and the hugging individuals in Figure 15 that pose a high threat to COVID-19 spread, the system fails to reflect this, deviating from the expert opinion. In Figure 16, the high proximity of the individuals in the first frame results in a high threat value for the first frame. However, the handshake interaction model fails to detect the interaction in the second frame, hence leading to a lower threat level output by the system and hence failing to identify the increase in threat for COVID-19 spread. In the case of Figure 17, since the system design was not accounted for incidents such as a pushing action as in the second frame, the system provides a higher threat value for the first frame contrary to human expert opinion.  
However, there were a few rare cases where in retrospect, the system output was more plausible or instances where the failure of the system was unexplained. Considering Figure 18, the ground truth from human expert opinion was that the threat level decreases, which is explained by the handshake interaction in the first frame, which is a serious violation of social distancing protocols. However, the system output for threat value increases significantly in the second frame as a new person is identified in the far left. Since an increase in the number of people and the closer proximity of this new person in a given space should also be accounted for, this leads to the increased threat value predicted by the system. Meanwhile, Figure 19 is an instance where the system output states the threat of COVID-19 spread has increased, whereas human expert opinion is on the contrary. This deviation by the system is an edge case where the deviation is unexplained.  
Figure 16: Failure case 3 threat level interpretations. System threat evaluation output—Decreases, Human expert opinion output—Increases.  
Figure 17: Failure case 4 threat level interpretations. System threat evaluation output—Decreases, Human expert opinion output—Increases.  
Figure 18: Edge case 1 threat level interpretations. System threat evaluation output—Increases, Human expert opinion output—Decreases.  
Figure 15: Failure case 2 threat level interpretations. System output for threat—Increases, Human expert opinion on threat—Decreases.