## 4 Experimental Results and Analysis
### Results of Three-Perspective Adaptive Cheating Behavior Detection  
To validate the proposed three-perspective adaptive cheating behavior detection method, we selected existing dual-perspective (overhead and horizontal, face and horizontal, and face and overhead) cheating behavior detection methods as comparison objects. We tested and evaluated the accuracy of cheating behavior detection on our dataset, and the results are summarized in Table 6. The accuracy calculation Formula (7) is as follows:  
Figure 14: Examples of cheating tool detection results from different perspectives. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Perspective** & **Indication** & **Model** & **Book** & **Paper** & **People** & **Phone** & **Average** \\ \hline \multirow{4}{*}{Overhead perspective} & \multirow{2}{*}{mAP50} & YOLOv5 & 99.3\% & 99.3\% & 99.5\% & 91\% & 97.3\% \\  & & Lightweight- & **99.5\%** & **99.3\%** & **99.5\%** & **91.5\%** & **97.4\%** \\ \cline{2-7}  & \multirow{2}{*}{mAP50:90} & YOLOv5 & 89\% & 88.6\% & 88.9\% & 71.2\% & 84.6\% \\  & & Lightweight- & **89.8\%** & **89.37\%** & **89.1\%** & **71.6\%** & **85.1\%** \\ \hline \multirow{4}{*}{Horizontal perspective} & \multirow{2}{*}{mAP50} & YOLOv5 & 99.2\% & 99.3\% & 99.4\% & 97\% & 98.7\% \\  & & Lightweight- & **99.3\%** & **99.3\%** & **99.4\%** & **97.1\%** & **98.8\%** \\ \cline{2-7}  & \multirow{2}{*}{mAP50:90} & YOLOv5 & 88.7\% & 88.4\% & 88.8\% & 75.4\% & 85.3\% \\ \cline{1-1}  & & Lightweight- & **89.7\%** & **89.6\%** & **89\%** & **77.4\%** & **86.4\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detection results of abnormal objects from different models in overhead perspective. True Positive (_TP_) is the true example, True Negative (_TN_) is the true negative example, False Positive (_FP_) is the false positive example, and False Negative (_FN_) is the false negative example,  
\[Accuracy=(TP+TN)/(TP+TN+FP+FN) \tag{7}\]  
Through comparison, it can be seen that the accuracy of the three existing dual-perspective cheating detection methods mentioned above is relatively low. The adaptive three-perspective cheating behavior detection method proposed in this paper avoids the misjudgment caused by a single detection to a certain extent, effectively expands the visual monitoring range, reduces detection blind spots, and improves the robustness and accuracy of cheating behavior detection. Figure 15 shows the detection results of eight common cheating behaviors. Each set of monitoring images consists of images from the perspectives of overhead, face, and horizontal. It can be seen that when the candidate's gaze direction is left, right, downleft, downright, or down, a student is often using a tool from the desktop to cheat; when the candidate's gaze direction is upleft, up, or upright, there is often a third party assisting in cheating. The monitoring images from the three perspectives can clearly and effectively describe the cheating behavior. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Monitor Perspective**} & **Overhead** & **Gaze** & **Horizontal Object** & **Cheating** \\  & & **Object** & **Recognition** & **Detection** & **Behavior** \\  & & & & **Determination** \\ \hline Ref. [38] & Overhead and Horizontal & 89.1\% & \(\times\) & – & 81.1\% \\ Ref. [12] & Face and Horizontal & \(\times\) & 82.9\% & 89\% & 65.3\% \\ Ref. [11] & Face and Overhead & 91.87\% & 68.9\% & \(\times\) & 81.83\% \\ Ref. [28] & Face and Overhead & 60\% & 89.95\% & \(\times\) & \(-\) \\ Ref. [10] & Face and Overhead & \(-\) & – & \(\times\) & 83.56\% \\ Ours & Overhead and Face and Horizontal & **92.4\%** & **98.8\%** & **95.7\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results of different cheating detection methods. Figure 15: Examples of detection results of adaptive cheating behavior from three perspectives.