## 5 Experiment
### Accuracy of Cloud Computing  
#### 5.2.1 Effect of People Formations on the Accuracy  
In this study, we designed a total of four formations for experimentation: a horizontal straight-line formation, a vertical straight-line formation, a diagonal straight-line formation, and a criss-cross formation. Through trials conducted in three distinct environments and from varying angles, we observed that, under all conditions, the recognition performance was optimal for the horizontal straight-line formation, while it was least effective for the vertical straight-line formation. The latter exhibited a reduced recognition performance due to the substantial overlap of target objects, rendering the MobileNet image model unable to identify the targets and consequently hindering subsequent SSD-based tracking, leading to counting inaccuracies. In contrast, the diagonal straight-line and criss-cross formations also exhibited target overlap, albeit with smaller coverage areas, allowing the image model to still recognize target categories and enabling subsequent tracking by algorithms.  
People sequentially entered the waiting area for the elevator. The results, as indicated in Tables 2-4, show that, for the same number of individuals, the camera angle of forty-five degrees obliquely downward provides a superior accuracy level compared to the horizontal angle. This angle choice mitigates the issue of obscured recognition when individuals are nearly invisible due to occlusions, thus resulting in higher accuracy and stability values across various formations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Number of People in** \\ **Waiting Area** \\ \end{tabular} & \begin{tabular}{c} **Formation** \\ **Horizontal** \\ \end{tabular} & \
\begin{tabular}{c} **Vertical** \\ **Diagonal** \\ \end{tabular} &
\begin{tabular}{c} **Class-Cross** \\ \end{tabular} \\ \hline
1 & 100\% & 100\% & 100\% & 100\% \\ \hline
2 & 100\% & 100\% & 100\% & 100\% \\ \hline
3 & 100\% & 100\% & 67\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy when counting three people sequentially entering the elevator waiting area.  
Figure 9: Camera angle and waiting area of an elevator.  
With an increase in the number of individuals within the image, apart from addressing occlusion-related issues, the image model must distinguish whether the detected targets are distinct objects to prevent double-counting or undercounting. Within the MobileNet-SSD object detector framework, the SSD algorithm employs the centroid tracker to identify the centroids of objects classified by MobileNet within the designated image regions and records them. If the centroid remains within a limited displacement range during subsequent object tracking, it is not considered a new object; conversely, a significant displacement indicates a new object. In our system, once a target enters the recognition area and is identified with a centroid, upon the centroid crossing the designated LOI segment, the system increases the count for that area. Conversely, if the centroid exits and crosses the LOI segment, the count for that area decreases. Throughout our experiments involving the four formations with varying numbers of individuals entering the recognition area, accurate recognition was consistently achieved, regardless of either the horizontal or forty-five-degree viewing angles. Concerning the formations, the accuracy was compromised for formations with pronounced overlap, such as the vertical and criss-cross formations, resulting in a comparatively lower accuracy. Thus, in scenarios where the image contains a higher number of target objects with increased occlusion, the accuracy of the MobileNet-SSD object detector is notably impacted.