# Three-Stage Deep Learning Framework for Video Surveillance
## 4 Experiment and Results
### Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Hockey Fights** & **Movie Fights** & **Crowd** \\ \cline{2-3}  & **ACC** & **ACC** & **ACC** \\ \hline Semi-Supervised & & & \\ Hard Attention & & & \\ (SSHA (RGB & 98.7 & 100 & - \\ only) [25] & & & \\ SepConvLSTM-A [29] & 99.0 & 100 & - \\ SepConvLSTM-C [29] & 99.5 & 100 & - \\ SepConvLSTM- & 99.0 & 100 & - \\ M [29] & 93.4 & 99.0 & - \\ STIFV [14] & 97.1 & 100 & - \\ Human Skeletons + & 94.5 & 98.5 & 94.3 \\ Change Detection [20] & 96.8 & 98.5 & 94.5 \\ SPIL Convolution [21] & 98.0 & 100 & 88.87 \\ Flow Gated & & & \\ Network [23] & 98.0 & 100 & 94.0 \\ VBiLT (Ours) & 97.67 & 100 & 93.24 \\ MobileBiLT (Ours) & 96.33 & 100 & 93.24 \\ DenseBiLT (Ours) & 97.67 & 100 & 93.24 \\ CvtBiLT (Ours) & 97.0 & 100 & 95.95 \\ CvlBiLT (Ours) & 98.0 & 100 & 95.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of result on each dataset. situations in both class-balanced and class-imbalanced datasets, thereby proving their effectiveness across various environments and conditions. This emphasizes the effectiveness of the proposed modified Transformer for anomaly detection and the flexibility and accuracy of the proposed three-stage framework for anomaly detection. In a more detailed examination of the results on the Movie Fights dataset, all the proposed frameworks achieved detection accuracy of 100%. On the Crowd dataset, CvtBiLT and CvlBiLT, which use ConvNeXt as the backbone with high model complexity, performed the best. For the Hockey Fights dataset, CvlBiLT achieved commendable performance of 98%. On the UBI-Fights dataset, which was measured using AUC as the primary metric, MobileBiLT, with the lightest model complexity, outperformed all other networks, followed by CvlBiLT, with the highest model complexity. In summary, CvlBiLT achieved the most superior performance on the three datasets closely related to CCTV footage, UBI-Fights, Movie Fights, and Crowd, demonstrating its effectiveness in environments most relevant to the intended application. Furthermore, it also exhibited the best performance among the proposed architectures in the additional validation dataset, Hockey Fights. When examining the average anomaly detection accuracy (AUC for UBI-Fight and ACC for the others) across the four datasets for the five proposed frameworks, the results were as follows: VBiLT achieved 97%, MobileBiLT achieved 96.88%, DenseBiLT achieved 96.77%, CvtBiLT achieved 97.71%, and CvlBiLT achieved 98.13%. This shows that CvlBiLT and CvtBiLT, which have high model complexity, perform the best. However, from a model complexity perspective, the VBiLT and MobileBiLT frameworks, which use lightweight models like ViT or MobileNet as backbones, are more effective choices. This indicates that these models can achieve high accuracy with low computational complexity. These results demonstrate that the proposed three-stage framework is highly useful for image analysis in various conditions and environments. The performance evaluation in this study provides an important benchmark for future research and development.