# A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras
## 4 Experimental Results
### Evaluation  
In our experiments, we used different kinds of evaluations. In the first step, We tested our model with several types of CNN models (VGG19, InceptionV3, ResNet50, ResNet 101, and ResNet 152) in keras library. Table 4 shows the comparison. Therefore, we selected ResNet50 for our experiments due to good accuracy with the less complicated structure. We compare our proposed method with a 3D convolutional network by measuring values; Accuracy (Acc) and Area under the curve (AUC). As the UCF-Crime dataset is relatively a new dataset, there are not too much work on that yet. Table 5 compares the AUC value for binary classification on the UCF-Crime dataset for our proposed method (ResNet50 ConvLSTM) and seven other models for anomaly detection such as, SVM, MIL [30], C3D [29], and TSN [64]. We consider all mentioned abnormal event categories only as one category, 'Anomaly', and other data with no abnormal events as 'Normal'. The test classifier shows the probability of the correct classification for abnormal events. Therefore, as shown in Table 5, our model outperformed the previous methods. Figure 5 depicts the training curve for binary classification with regards to accuracy and loss value. In case of accuracy, we also compared our model with C3D by considering all 14 categories (13 abnormal events plus one normal event). In order to illustrates how the proposed model worked on the UCF-Crime dataset, we evaluate the method by calculating _precision_, _recall_, and _F1-score_ that can be seen in Table 6. Furthermore, the provided confusion matrix in Figure 6 shows the classification details. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Acc (\%)** \\ \hline ResNet50 ConvLSTM & 62.5 \\ InceptionV3 ConvLSTM & 62.5 \\ VGG19 ConvLSTM & 59.32 \\ ResNet101 ConvLSTM & 63.75 \\ ResNet152 ConvLSTM & 56.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison among different type of CNN in keras for binary classification. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper Parameters** & **Tune** & **Acc (\%)** \\ \hline Data Augmentation & True & 54.18 \\ Data Augmentation & False & 53.82 \\ \hline Initial Weight & glorot\_uniform & 63.88 \\ Initial Weight & random\_uniform & 54.17 \\ Initial Weight & he\_uniform & 54.17 \\ \hline Optimizer & RMSprop & 63.88 \\ Optimizer & Adam & 61.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tuning hyper parameters for binary classification on the UCF-Crime dataset. In that case, as the Uccfrimes have many everyday actions in real situations with different lighting and angles, it is difficult to distinguish them with an outstanding accuracy value. The proposed method works relatively as good as C3D. However, this sophisticated method is not suitable for the UCF-Crime dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Metric** & **(Value\%)** \\ \hline \hline \multicolumn{2}{c}{_Precision_} & \multicolumn{2}{c}{22.93} \\ \multicolumn{2}{c}{_Recall_} & \multicolumn{2}{c}{24.31} \\ \multicolumn{2}{c}{_F1-score_} & \multicolumn{2}{c}{23.60} \\ \multicolumn{2}{c}{_Accuracy_} & \multicolumn{2}{c}{22.72} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation for 14 categories classification on the UCF-Crime dataset. Figure 5: Training curve for binary classification. Figure 6: Confusion matrix for 14 categories classification on the UCF-Crime dataset. dataset structure might need a more complicated design for feature extraction. It can be happened by using more convolutional layers to access more high-level features. In this case, we used ResNet101 instead of ResNet50 in our proposed model, which increased the classification accuracy. Therefore, by using ResNet101, our model, we achieved a fractionally better accuracy over the C3D model in our experiments. Table 7 shows this comparison. As we cannot achieve an outstanding accuracy in classifying all types of anomalies in the Ucfcrimes dataset, we categorized them into four major groups: Theft, Vandalism, Violence, and Normal behaviours.