## 5 Experiments
### Case Studies with Visualizations  
To gain further insight into the effectiveness of the proposed model, Figures 7-11 display the anomaly scores for different videos from the three datasets with some key normal and abnormal frames, with the anomaly ground truth shaded in pink. Two dif \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Model** & **UCSD-Ped2 (\%)** & **Avenue (\%)** & **ShanghaiTech (\%)** \\ \hline \multirow{8}{*}{Recon.} & [87] & Two-stream autoencoder & 84.5 & 80.3 & - \\ \cline{2-5}  & [58] & Attention-based ConvLSTM & 95.6 & - & 73.1 \\  & network p Conv Autoencoder & 97.9 & 85.9 & - \\ \cline{2-5}  & [71] & AE + DPU + Attention module & 97.9 & 85.9 & - \\ \cline{2-5}  & [88] & Implicit two-path AE + & 97.3 & 85.8 & 74.7 \\  & normalizing flow & 97.9 & 86.2 & - \\ \cline{2-5}  & [56] & Noise AE + AE + Second-order & 95.4 & 80.9 & - \\ \cline{2-5}  & [76] & ConvLSTM-AE & 97.9 & **89.8** & 73.7 \\ \cline{2-5}  & [77] & Transformer memory + AE & 98.1 & 88.5 & 72.5 \\ \hline \multirow{4}{*}{[66]} & Multi-scale feature attention & & & \\  & U-Net + weakly supervised & 97.9 & 86.2 & - \\ \cline{1-1}  & data augmentation & & & \\ \cline{1-1} \cline{2-5}  & Ours & ViT + STR attention & 95.6 & 86.8 & **82.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Cam 1** & **Cam 2** \\ \hline TSGAD ([PERSON] et al., 2020) & 63.2 & 60.1 \\ GEPC ([PERSON] et al., 2020) & 63.0 & 63.0 \\ STG-NF ([PERSON] et al., 2020) & 61.5 & 56.2 \\ Proposed 1 & 71.8 & 64.2 \\ \hline \hline \end{tabular} \({}^{\dagger}\)Our model was trained on a combined set of Cam 1 and Cam 2.  
\end{table} 
Table 8: A comparison of AUC ROCs (%) for different models trained on the CHAD dataset. ferent anomaly scores from each of the Ped2 and Avenue datasets are displayed, as the two datasets represent single-scene videos (taken from a fixed position camera from a fixed view angle). For the ShanghaiTech dataset, there are thirteen distinct scenarios; hence, we display three different scenes with two situations for each. In addition, we show two different videos for each Cam 1 scene and Cam 2 scene from the CHAD dataset. The proposed approach responds successfully to anomaly event occurrences that are shown by the rising anomaly scores during the anomaly periods and the falling anomaly scores for the regular situations. For instance, Figure 7 depicts the anomaly scores for test videos 02 and 04 from the UCSD-Ped dataset. When the cyclist's anomalous event, shaded in pink, happened in Figure 7a, the score increased and remained high until it reached the end of the anomaly period. Furthermore, Figure 7b shows how the anomaly score rises as soon as the anomaly object, the cart, enters the picture and stays high for the duration that the anomaly objects, the cart and the cyclist, remain in the scene. Moreover, Figure 8 displays the anomaly scores for test clips 05 and 06 from the Avenue dataset. The anomaly score in Figure 8a indicates that the anomalous event \"playing with the bag\" is correctly identified. The anomalous events in test video 06 (throwing bag and wrong direction) may be seen in Figure 8b during four periods, three of which are very short and represent a guy throwing a bag. Despite this, the proposed model was able to identify the anomalous times. Figure 7: Anomaly score curves of two different videos for UCSD-Ped2 dataset: (**a**) Test 02 (anomaly type: cyclist); (**b**) Test 04 (anomaly type: cart and cyclist). Figures 9-11 represent the anomaly scores for test videos from three distinct scenes from the challenging ShanghaiTech dataset. The anomaly scores in the figures demonstrate how the anomaly events/periods in every video were identified successfully with high scores. The presence of the motorcyclist in the scene raised the abnormality score in test video 0016 from scene 01, as shown in Figure 9a, which is the same behavior that occurred in Figure 9b with the biker's existence in test video 0177 from the same scene.