## 5 Experiments
### Comparative Methods  
As demonstrated in Table 7, the proposed method achieved comparable performance for both the UCSD and Avenue datasets compared to the state-of-the-art (SOTA) approaches. Notably, the two datasets are characterized by small and medium size, respectively, and the model was designed to deal with the large datasets. For the Shanghai University of Science and Technology dataset, there are thirteen distinct complicated contexts and situations, including dense crowds, varied movement patterns, and unusual occurrences. Nevertheless, the proposed model showed a 7.3% increase in detection performance over existing VAD methods on this dataset, which is considered the large-scale benchmark dataset currently available in the video anomaly detection domain. The proposed method's power is demonstrated by its comparison with other recent SOTA methods, such as the spatiotemporal convolutional autoencoder model introduced by [PERSON] and [PERSON] [76] and the transformer memory autoencoder approach used by [PERSON] et al. [77], which yielded detection superiorities of 8.4% and 9.6% for the Shanghai dataset, respectively. In addition, it achieved a superior result compared to the recent work in [66] which employed an attention U-Net based on multi-scale feature extraction with a data augmentation network, achieving +0.6% for the Avenue dataset, but the authors did not use the Shanghai dataset in their experiments. As a result, the proposed model made progress towards finding a high-detection performance solution to the problem of finding abnormalities in large, diverse, and heterogeneous anomaly datasets. Therefore, this research may contribute to the development of reliable real-time video anomaly detection systems. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & **Model** & **UCSD-Ped2 (\%)** & **Avenue (\%)** & **ShanghaiTech (\%)** \\ \hline \multirow{8}{*}{Non-Recon.} & \multirow{3}{*}{[78]} & Multivariate Gaussian Fully & \multirow{3}{*}{91.6} & \multirow{3}{*}{84.2} & \multirow{3}{*}{-} \\  & & Convolution Adversarial & & & \\  & & Autoencoder (MGFC-AAE) & & & \\ \cline{2-5}  & [79] & FSM-GAN & 98.1 & 80.1 & 73.5 \\ \cline{2-5}  & [80] & Two U-Net generators + discriminator & 96.3 & 85.1 & 73.0 \\ \cline{2-5}  & [19] & Memory-guided AE & 97.0 & 88.5 & 70.5 \\ \cline{2-5}  & [81] & Spatiotemporal consistency & \multirow{3}{*}{96.9} & \multirow{3}{*}{86.6} & \multirow{3}{*}{73.8} \\  & enhanced network (STCEN) & & & \\ \cline{2-5}  & [75] & Attention residual AE & 97.4 & 86.7 & 73.6 \\ \hline \multirow{8}{*}{Recon.} & [82] & Memory-augmented AE & 94.1 & 83.3 & 71.2 \\ \cline{2-5}  & [83] & AE + Probability distribution & \multirow{3}{*}{95.4} & \multirow{3}{*}{-} & \multirow{3}{*}{72.5} \\  & density estimator & & & \\ \cline{2-5}  & \multirow{3}{*}{[84]} & Gaussian Mixture Fully & \multirow{3}{*}{92.2} & \multirow{3}{*}{83.4} & \multirow{3}{*}{-} \\  & & Convolutional Variational & \multirow{3}{*}{96.5} & \multirow{3}{*}{86.0} & \multirow{3}{*}{73.3} \\  & & Autoencoder (GMFC-VAE) & & & \\ \cline{1-1} \cline{2-5}  & [85] & Two-stream clustering Autoencoder & 96.5 & 86.0 & 73.3 \\ \cline{1-1} \cline{2-5}  & [86] & Residual autoencoder & 83.0 & 82.0 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Frame-level AUC performance comparison of the proposed framework against state-of-the-art anomaly detection methods for the UCSD, Avenue, and Shanghai datasets. Table 8 shows a comparison of the introduced framework against the state of the art. There are few works that employed the CHAD for video anomaly detection because the CHAD is a recent dataset, for example, [PERSON] et al. (2020) evaluated some pose-based techniques on this dataset. From Table 8, it is obvious that our ViT + STRA framework outperforms these techniques, taking into consideration that the TSGAD ([PERSON] et al., 2020), GEPC ([PERSON] et al., 2020), and STG-NF ([PERSON] et al., 2020) techniques were trained on Cam 1 or Cam 2 individually.