# FenceTalk: Exploring False Negatives in Moving Object Detection
## 3 FenceTalk Architecture
In the testing phase, FenceTalk's precision was 98.57% and its recall was 99.67%. Both cases (Tables 2 and 3) indicated that integrating SSIM into FenceTalk led to a further improvement in the overall F1 score compared to using only the Yolo model for recognition. Figure 9 presents the precision accuracy of Yolo and SSIM under different sub-datasets and brightness levels. For Dataset 1, in the training phase, the lowest precision was 88.24% at brightness level 93. In the validation phase, the lowest precision was 87.34% at brightness level 84. In the testing phase, the lowest precision was 93.56% at brightness level 83. For Dataset 2, in the training phase, the lowest precision was 89.89% at brightness level 82. In the validation phase, the lowest precision was 90.21% at brightness level 86. In the testing phase, the lowest precision was 91.46% at brightness level 86. Figure 10 depicts the recall accuracy of Yolo and SSIM under different sub-datasets and brightness levels. For Dataset 1, in the training dataset, the lowest recall was 95.24% at brightness level 89. In the validation phase, the lowest recall was 97.37% at brightness level 94. In the testing dataset, the lowest recall was 99.22% at brightness level 84. For Dataset 2, in the training phase, the lowest recall was 95.31% at brightness level 101. In the validation phase, the lowest recall was 98.77% at brightness level 89. In the testing phase, the lowest recall was 98.45% at brightness level 98. Figures 9 and 10 display the lowest precision and recall values across various sub-datasets and brightness levels. These lowest precision and recall metrics represent the baseline performance of FenceTalk. In the majority of cases, FenceTalk's performance exceeded these lower bound values. We also conducted a comparison of how ViT and SSIM performed in detection of moving objects. We applied ViT and SSIM in greenhouse equipment operation status \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset 2**} & \multicolumn{3}{c}{**Yolo**} & \multicolumn{3}{c}{**SSIM**} & \multicolumn{3}{c}{**Yolo + SSIM**} \\ \cline{2-10}  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline Training dataset & & & & & & & & & \\ (optimal SSIM & & & & & & & & \\ thresholds from & & & & & & & & & \\ training dataset) & & & & & & & & \\ \hline Validation dataset & & & & & & & & & \\ (optimal SSIM & & & & & & & & \\ thresholds from & & & & & & & & \\ training dataset) & & & & & & & & \\ \hline Validation dataset & & & & & & & & \\ (optimal SSIM & & & & & & & & \\ thresholds from & & & & & & & & \\ training dataset) & & & & & & & & \\ \hline Test dataset & & & & & & & & \\ (optimal SSIM & & & & & & & & \\ thresholds from & & & & & & & & \\ training dataset) & & & & & & & & \\ \hline Test dataset & & & & & & & & & \\ (optimal SSIM & & & & & & & & \\ thresholds from & & & & & & & & \\ validation dataset) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The output measures for Dataset 2 (red font signifies the best performance). detection. For instance, when a user turned on the exhaust fan (Figure 11), we checked whether the fan started correctly to determine the equipment's normal operation. Table 4 displays the performance of ViT in recognizing equipment operation status. ViT achieved a recall of 1 and an F1 score of 0.999. Table 5 presents the results of SSIM, which were slightly lower than those of ViT. Specifically, SSIM achieved a recall of 0.98 and an F1 score of 0.989. These results indicate that SSIM can provide satisfactory performance when applied to moving object detection. However, when compared to ViT, SSIM requires significantly fewer computational resources. Figure 12 illustrates the processing speed and GPU utilization of the embedded device Jetson Nano when executing FenceTalk. Each instance of FenceTalk was capable of performing image recognition for an RTSP streaming camera.