# Three-Stage Deep Learning Framework for Video Surveillance
## 4 Experiment and Results
### Results  
We named our proposed baseline framework \"BiLT\", which indicates the combination of BiLSTM, multi-head attention, and the Transformer Encoder. We proposed five different network architectures based on BiLT and compared their performance to existing methodologies. Each network was designed considering specific objectives and environments, and their characteristics are as follows. 1. VBiLT (Vision Transformer-based BiLT): This architecture uses Vision Transformer as the feature extractor. It effectively captures the global context and can learn features by emphasizing important parts through the attention mechanism. 2. MobileBiLT (MobileNet-based BiLT): In this architecture, MobileNet [44] is used as the feature extractor. It inherits MobileNet's characteristics to provide reasonable accuracy with reduced computational complexity. 3. DenseBiLT (DenseNet-based BiLT): This architecture utilizes DenseNet [45] as the feature extractor. It takes advantage of DenseNet's feature reuse mechanism, allowing it to preserve the initial features by utilizing information from previous layers. 4. CvtBiLT (ConvNeXt Tiny-based BiLT): ConvNeXt Tiny is used as the feature extractor in this architecture. ConvNeXt [46] is inspired by Transformer models and enhances the performance of traditional convolutional networks. This architecture provides high performance at a reasonable computational cost. 5. CvlBiLT (ConvNeXt Large-based BiLT): In this architecture, ConvNeXt Large is employed as the feature extractor. ConvNeXt Large has more parameters and deeper layers compared to ConvNeXt Tiny, making it suitable for handling complex and diverse data. The performance evaluation in this study was conducted through a comparison with existing networks. To facilitate this comparison, we adopted the same performance metrics as those used in the previous networks investigated in this work, ensuring consistency in the evaluation criteria. In all studies utilizing the UBI-Fights dataset, due to its imbalanced nature, model performance comparisons were made using the Area Under the Curve (AUC) metric, which better reflects this imbalance, instead of accuracy. For studies employingthe Hockey Fights, Movie Fights, and Crowd datasets, model performance comparisons were conducted using accuracy, as it effectively measures the classification precision. As mentioned in Section 4.1, the proposed model underwent training and validation using the divided training and validation datasets. The results of validating the proposed architecture using the UBI-Fights dataset, followed by a comparison of the AUC performance with existing networks, are presented in Table 2.  
Additionally, the outcomes of training and validating the proposed architecture with the Hockey Fights, Movie Fights, and Crowd datasets, followed by a comparison of the accuracy (ACC) performance with existing networks, are shown in Table 3.  
According to the performance evaluation results, all five proposed frameworks demonstrated superior performance on the UBI-Fights dataset compared to existing networks. Additionally, in alignment with the main objective of this study--detecting anomalies in CCTV footage--it was confirmed that CvlBiLT exhibited the most impressive classification performance on the Movie Fights and Crowd datasets. This underscores the capability of the proposed frameworks to accurately distinguish between normal and abnormal \begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Method**} & **UBI-Fights** \\ \cline{2-3}  & **AUC** \\ \hline [PERSON] et al. [4] & 89.2 \\ SS-Model [24] & 81.9 \\ SS-Model + WS-Model + [PERSON] et al. [24] & 84.6 \\ GMM [2] & 90.6 \\ VBiLT (Ours) & 96.33 \\ MobileBiLT (Ours) & 97.96 \\ DenseBiLT (Ours) & 96.16 \\ CvtBiLT (Ours) & 97.87 \\ CvlBiLT (Ours) & 98.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of result on UBI-Fights dataset.