## 4 Experimental Results
### Comparing Deep Learning Approaches  
The experiments conducted to compare the six investigated approaches achieved the results summarized in Table 3. Area under the ROC curve (AUC), accuracy, precision and recall were calculated to determine the performance of the approaches. In addition, the number of processed frames per second (FPS) was also reported to compare the time cost. Values in bold indicate the best result in each dataset. Although the mosaic simulation in the DenserGlitch dataset was expected to be more realistic, all approaches reached the highest possible performance in this dataset, except for the autoencoder. The justification for this high performance in DenserGlitch when compared to the results reached on the other datasets is that DenserGlitch is less complex in nature, as it presents only one type of anomaly (mosaic) and the anomalous samples present a mosaic in all frames. For the two datasets presenting temporal and spatial defeats, the proposed 3D-GAN achieved a better area under the curve and higher accuracy and recall compared to the supervised methods pre-trained and customized 3D-CNN (cust. 3D-CNN) in the 60 frames dataset. In terms of the 1R dataset, 3D ResNet was superior while 3D-GAN, C3D and cust. 3D-CNN provided equivalent performances. It is interesting to observe the results attained by MoViNet-A2. Despite being a lightweight architecture, we can see in Table 3 a 12% accuracy reduction in the 1R dataset compared to the best-performing model (3D ResNet). However, in the 60 frames dataset we see a much larger accuracy difference. It is important to mention that larger MoViNets (A3-A5) would probably reduce this difference since it has been shown that MoViNet-A5 attains state-of-the-art results in different applications [20]. These results show that the proposed custom generator module seems to be steadily beneficial for the anomaly detection task using 3D-GAN, since this approach consistently achieved high performance in all datasets, even when only using 25% of labeled anomalous instances, whilst 3D ResNet, C3D, MoViNet and the cust. 3D-CNN were trained using 100% of labeled anomalous instances. This is a very competitive advantage as it avoids the need for a large set of manually labeled anomalous data. The results achieved by the autoencoder, on the other hand, were significantly worse than those of the other five methods. Despite being expected, since autoencoder is an unsupervised approach, we believe this performance may still be increased by better fine-tuning \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Model** & **AUC** & **ACC** & **Precision** & **Recall** & **FPS** \\ \hline \multirow{8}{*}{DenserGlitch} & Cust. 3D-CNN & **1.0000** & **1.0000** & **1.0000** & **1.0000** & 242 \\  & C3D & **1.0000** & **1.0000** & **1.0000** & **1.0000** & 196 \\  & MoViNet & **1.0000** & **1.0000** & **1.0000** & **1.0000** & 179 \\  & 3D ResNet & **1.0000** & **1.0000** & **1.0000** & **1.0000** & 229 \\  & AutoEncoder3D & 0.8333 & 0.9537 & 0.9152 & **1.0000** & 72 \\  & 3D-GAN & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **400** \\ \hline \multirow{8}{*}{60 frames} & Cust. 3D-CNN & 0.9450 & 0.8660 & **1.0000** & 0.8220 & **476** \\  & C3D & 0.9772 & 0.9775 & 0.9920 & 0.9779 & 211 \\  & MoViNet & 0.7755 & 0.6631 & **1.0000** & 0.5510 & 215 \\  & 3D ResNet & 0.9714 & 0.9572 & **1.0000** & 0.9429 & 382 \\  & AutoEncoder3D & 0.5519 & 0.7623 & 0.7705 & 0.9728 & 185 \\  & 3D-GAN & **0.9896** & **0.9909** & 0.9957 & **0.9921** & 240 \\ \hline \multirow{8}{*}{1R} & Cust. 3D-CNN & 0.9581 & 0.9586 & 0.9984 & 0.9176 & **395** \\  & C3D & 0.9578 & 0.9579 & 0.9736 & 0.9414 & 181 \\ \cline{1-1}  & MoViNet & 0.8570 & 0.8583 & **0.9988** & 0.7149 & 198 \\ \cline{1-1}  & 3D ResNet & **0.9825** & **0.9826** & 0.9925 & **0.9723** & 217 \\ \cline{1-1}  & AutoEncoder3D & 0.6892 & 0.6897 & 0.8297 & 0.4756 & 122 \\ \cline{1-1}  & 3D-GAN & 0.9530 & 0.9450 & 0.9930 & 0.8990 & 237 \\ \hline \hline \end{tabular}
\end{table}