# Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
## 6 Results and Discussion  
The section presents the results of the experimentation conducted on the proposed dataset. Table 2 shows the accuracy and precision. Recall and F\({}_{1}\) were obtained by applying various methods for shoplifting detection. In the Table, \"Methods\" refers to the applied technique for the experiment used for shoplifting detection. Overall, the best result with (accuracy = 82%, precision = 88.80%, recall = 78.40 %, and F\({}_{1}\) = 83.01%) has been shown by our proposed method for the shoplifting detection task. It can also be observed that the proposed model is outperforming in terms of all measures with respect to accuracy, precision, recall, and F\({}_{1}\). Tables 3 and 4 show the confusion matrices, false positive rate, false negative rate, false negative rate, etc., for 2D CNN and 3D CNN, respectively. Similarly, Table 5 shows the confusion matrix for more insight details of our proposed model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{\begin{tabular}{c} **Convolutional** \\ **Layers** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Activation** \\ **Units** \\ \end{tabular} } & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} & \multirow{2}{*}{\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **F\({}_{1}\)** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{**AUC**} \\ \hline
2D CNN & 4 &
\begin{tabular}{c} Relu \\ Softmax \\ \end{tabular} & 50.00 & 45.00 & 51.00 & 50.00 & 50.40 & 0.49 \\
3D CNN & 8 &
\begin{tabular}{c} Relu \\ Softmax \\ \end{tabular} & 60.85 & 55.38 & 66.60 & 58.80 & 61.80 & 0.57 \\
**Proposed** & **16** & **Relu** **Softmax** & **82.01** & **81.00** & **88.80** & **78.40** & **83.01** & **0.88** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of baseline methods and proposed method in terms of accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{Actual} & \multicolumn{4}{c}{Predicted} \\  & & \multicolumn{2}{c}{Shoplifting} & \multicolumn{2}{c}{Non-Shoplifting} \\  & & \multicolumn{2}{c}{230} & \multicolumn{2}{c}{220} \\  & & Non-Shoplifting & 230 & 220 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion Matrix of 3D CNN. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{\begin{tabular}{c} **Convolutional** \\ **Layers** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Activation** \\ **Units** \\ \end{tabular} } & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} & \multirow{2}{*}{\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **F\({}_{1}\)** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{**AUC**} \\ \hline
2D CNN & 4 &
\begin{tabular}{c} Relu \\ Softmax \\ \end{tabular} & 50.00 & 45.00 & 51.00 & 50.00 & 50.40 & 0.49 \\
3D CNN & 8 &
\begin{tabular}{c} Relu \\ Softmax \\ \end{tabular} & 60.85 & 55.38 & 66.60 & 58.80 & 61.80 & 0.57 \\
**Proposed** & **16** & **Relu** **Softmax** & **82.01** & **81.00** & **88.80** & **78.40** & **83.01** & **0.88** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion Matrix of 2D CNN. It shows that our proposed model based on transfer learning, which is a combination of Inception V3 + BILSTM, is more fruitful for shoplifting detection. The reasons for the best performance are as follows:  
1. Multi-scale processing to process images at different scales. This allows the network to capture features at different levels of abstraction, which helps improve its accuracy. 2. Inception V3 uses a combination of convolutional layers of different sizes and pooling layers to reduce the number of parameters in the network while still maintaining high accuracy.