# FenceTalk: Exploring False Negatives in Moving Object Detection
## 3 FenceTalk Architecture
If there were multiple highest recall values for a particular brightness level, we chose the threshold with the best precision among them. If duplicates remained, we selected the median as the optimal threshold. We employed the modified Yolo model trained on the training dataset to test the validation dataset and find the optimal SSIM threshold for different brightness levels in this dataset. Using the optimal SSIM threshold from the validation dataset, we identified images containing moving objects and images detected by the Yolo model. After manual labeling, these images were combined with all images containing humans from the training dataset, serving as training data for the Yolo model, which was then applied to the testing dataset for inferencing. Table 2 presents the precision and recall of Yolo and SSIM using the optimal thresholds in Dataset 1. In the training phase, the recall for Yolo was relatively low at 77.16%. However, through the FenceTalk mechanism, the FenceTalk precision (Yolo + SSIM) was 97.71% and the recall (Yolo + SSIM) was 98.68%. The validation phase showed that FenceTalk's precision exceeded 97% and FenceTalk's recall (Yolo + SSIM) was above 99%. We re-trained the Yolo model after the validation phase. Therefore, the validation phase was a second training phase. Then, in the testing phase, FenceTalk's precision was 97.65% and its recall was 99.75%. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset 1**} & \multicolumn{3}{c}{**Yolo**} & \multicolumn{3}{c}{**SSIM**} & \multicolumn{3}{c}{**Yolo + SSIM**} \\ \cline{2-10}  & **Precision** & **Recall** & \begin{tabular}{c} **F1** \\ **Score** \\ \end{tabular} & **Precision** & **Recall** & \begin{tabular}{c} **F1** \\ **Score** \\ \end{tabular} & **Precision** & **Recall** & \begin{tabular}{c} **F1** \\ **Score** \\ \end{tabular} \\ \hline \begin{tabular}{c} Training dataset \\ (optimal SSIM \\ thresholds from \\ training dataset) \\ \end{tabular} & _97.71\%_ & 77.16\% & 86.21\% & 97.99\% & 94.09\% & 96.00\% & 97.71\% & 98.68\% & 98.19\% \\ \hline \begin{tabular}{c} Validation dataset \\ (optimal SSIM \\ thresholds from \\ training dataset) \\ \end{tabular} & _97.2\%_ & 92.42\% & 93.63\% & & & & & \\ \hline \begin{tabular}{c} Validation dataset \\ (optimal SSIM \\ thresholds from \\ training dataset) \\ \end{tabular} & _97.59\%_ & 95.19\% & 96.37\% & 97.72\% & 99.64\% & 98.67\% \\ \hline \begin{tabular}{c} Test dataset \\ (optimal SSIM \\ thresholds from \\ training dataset) \\ \end{tabular} & _96.15\%_ & 92.93\% & 94.51\% & 97.64\% & 99.54\% & 98.58\% \\ \hline \
\begin{tabular}{c} Test dataset \\ (optimal SSIM \\ thresholds from \\ validation dataset) \\ \end{tabular} & _97.67\%_ & 93.53\% & 95.59\% & & & & \\ \hline
\begin{tabular}{c} Test dataset \\ (optimal SSIM \\ thresholds from \\ validation dataset) \\ \end{tabular} & _96.29\%_ & 93.81\% & 95.03\% & 97.65\% & 99.75\% & 98.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The output measures for Dataset 1 (red font signifies the best performance). Figure 10: FenceTalk recall performance under various luminance (brightness) levels. Yolo's core technology enables efficient and accurate real-time object detection, making it a fundamental tool in various computer vision applications. Yolo revolutionized object detection by introducing the concept of single-shot detection, meaning it can detect and classify objects in an image in a single forward pass of a neural network. Yolo places a strong emphasis on maintaining good precision, which in turn results in a lower recall. Yolo provides a confidence threshold to adjust the level of recall. A lower confidence threshold can achieve higher recall but may lead to lower precision. In Tables 2 and 3, we show fine-tuning of the confidence threshold of Yolo to achieve a similar precision level as that of FenceTalk. Subsequently, we compared the differences in recall between Yolo and FenceTalk (Yolo + SSIM). Table 3 presents the precision and recall of Yolo and SSIM using the optimal thresholds for Dataset 2.