## 5 Results
### Comparison with Existing Approaches  
To validate the effectiveness of our proposed methodology, we conducted an extensive experimental variations. In addition to these experiments, we performed a comparative analysis between the TempoFuseNet framework and existing state-of-the-art approaches that center around multiclass anomaly classification, using the UCF-Crime dataset as our testing ground. In a similar context, [PERSON] et al. (2018) introduced a convolutional neural-network-based approach that initiates with video preprocessing to create 3D cubes through spatial augmentation. To streamline the analysis process, they employed a subset of the dataset: eliminating extraneous data and manually identifying atypical segments to ensure class balance. Subsequently, these 3D video cubes were fed into a convolutional neural network (CNN) to extract spatiotemporal features. Their analysis of the UCF-Crime dataset yielded a classification accuracy of 45% across fourteen distinct classes. In another study, [PERSON] et al. (2019) employed a fuzzy-rule-based approach for video summarization with the aim of addressing issues related to excessive data and high computational costs. [PERSON] \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Class** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **FPR** & **FNR** \\ \hline Abuse & 92.59\% & 70.00\% & 70.00\% & 70.00\% & 95.77\% & 4.23\% & 30.00\% \\ Arrest & 95.06\% & 100.00\% & 60.00\% & 75.00\% & 100.00\% & 0.00\% & 40.00\% \\ Arson & 90.12\% & 66.67\% & 54.55\% & 60.00\% & 95.71\% & 4.29\% & 45.45\% \\ Assault & 91.36\% & 63.64\% & 70.00\% & 66.67\% & 94.37\% & 5.63\% & 30.00\% \\ Burglary & 93.83\% & 77.78\% & 70.00\% & 73.68\% & 97.18\% & 2.82\% & 30.00\% \\ Explosion & 91.36\% & 63.64\% & 70.00\% & 66.67\% & 94.37\% & 5.63\% & 30.00\% \\ Fight & 92.59\% & 66.67\% & 80.00\% & 72.73\% & 94.37\% & 5.63\% & 20.00\% \\ Normal & 91.36\% & 61.54\% & 80.00\% & 69.57\% & 92.96\% & 7.04\% & 20.00\% \\ Macro-Average & 92.28\% & 71.24\% & 69.32\% & 69.29\% & 95.59\% & 4.41\% & 30.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performance for super-resolution videos. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Class** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **FPR** & **FNR** \\ \hline Abuse & 86.42\% & 45.45\% & 50.00\% & 47.62\% & 91.55\% & 8.45\% & 50.00\% \\ Arrest & 93.83\% & 100.00\% & 50.00\% & 66.67\% & 100.00\% & 0.00\% & 50.00\% \\ Arson & 87.65\% & 55.65\% & 45.45\% & 50.00\% & 94.29\% & 5.71\% & 54.55\% \\ Assault & 86.42\% & 45.45\% & 50.00\% & 47.62\% & 91.55\% & 8.45\% & 50.00\% \\ Burglary & 91.36\% & 71.43\% & 50.00\% & 58.82\% & 97.18\% & 2.82\% & 50.00\% \\ Explosion & 90.12\% & 60.00\% & 60.00\% & 60.00\% & 94.37\% & 5.63\% & 40.00\% \\ Fight & 86.42\% & 46.15\% & 60.00\% & 52.17\% & 90.14\% & 9.86\% & 40.00\% \\ Normal & 86.42\% & 46.67\% & 70.00\% & 56.00\% & 88.73\% & 11.27\% & 30.00\% \\ Macro-Average & 88.58\% & 58.84\% & 54.43\% & 54.86\% & 93.48\% & 6.52\% & 45.57\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification performance for bicubic interpolation of videos. Figure 7: Confusion matrix for super-resolution videos. et al. [60] achieved a classification accuracy of 53% in their classification experiment by leveraging a hybrid slow-fast neural network. On the other hand, our study utilized a trimmed UCF-Crime dataset comprising eight classes and fifty videos. For the anomaly classification task, we applied a two-step approach: First, we upscaled the low-resolution (LR) videos using bicubic interpolation and an encoder-bank-decoder configuration for super resolution. The encoder and decoder played pivotal roles in downscaling and upscaling, while the bank was a pre-trained StyleGAN acting as a latent feature repository to enhance super-resolution performance based on feature priors.