# Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
## 4 Methods
### Baseline Methods
#### 4.1.1 Two-Dimensional Convolutional Neural Network  
A sort of neural network design known as a 2D convolutional neural network (CNN) is frequently used in image analysis tasks such as picture categorization, object recognition, and segmentation. A 2D array representing an image is commonly used as the input data for a 2D CNN. Each pixel in the array is represented by a numerical value that corresponds to the intensity or color of the associated pixel in the picture. The network comprises a number of layers, including convolution, pooling, and fully connected layers, which carry out different operations on the input data. A 2D CNN's convolutional layers identify edges, corners, and other patterns in the input picture as features. Multiple filters, or tiny matrices of weights, are included in each convolutional layer and are applied to the input picture to produce a collection of feature maps. These feature maps draw attention to various facts of the input image and assist the network in developing its pattern and object recognition capabilities. Downsampling the feature maps and reducing the dimensionality of the input data are accomplished using pooling layers in a 2D CNN. This makes the network more effective by lowering its computational complexity. A prediction is then made using a 2D CNN's fully connected layers based on the output of the earlier levels. These layers are frequently applied in the network's final stages to divide the input picture into one or more categories. In general, a 2D CNN is an effective tool for processing image data and has been applied in a variety of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasetsets** & **Shoplifting** & \begin{tabular}{c} **Non-** \\ **Shoplifting** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Videos** \\ \end{tabular} & \
\begin{tabular}{c} **Dataset** \\ **Length** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Fames/s** \\ \end{tabular} \\ \hline [PERSON] et al. [1] & 155 & 755 & 910 & 2730 s & 10 \\ [PERSON] et al. [2] & 87 & 88 & 175 & 1750 s & 15 \\ UCF-Crime [3] & 28 & 1872 & 1900 & 460,800 s & 30 \\
**Developed Dataset** & **450** & **450** & **900** & **2700 s** & **30** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of developed dataset with existing datasets of shoplifting. Figure 6: A boy putting the item in trouser pocket. fields, including computer vision [20], imaging in the medical field [21], and autonomous cars [22]. In our case, we have utilized a 2D model to train a dataset that identifies shoplifting. Our training set consists of 81,000 images, which are separated into two distinct classes. During training, the images are inputted with a size of 600 \(\times\) 600 and are passed from the input layer. In the next step, 4 Convolutional layers were used to extract low-level features for learning from images. After each convolutional layer, an activation layer was added to the network to learn more complex features and relationships between different parts of the input image with the RELU activation function. In the final step, the softmax activation function [23,24] was used for the final prediction. The 2D arrays of pixels that make up a picture are intended for analysis by 2D CNNs, which do not account for the 3D structure of the real world. They may, therefore, be unable to accurately depict the spatial connections between items in a picture. A 2D CNN's topology is normally determined before training, and the network is tuned to carry out a particular task. This implies that for the analysis of complicated or changeable data, 2D CNNs could not be as adaptable as other types of models. The quality of the picture can alter due to changes in lighting, color, and orientation, which 2D CNNs are sensitive to. As a result, they might not perform as well on pictures that are not the same as the ones they were trained on. Additionally, a 2D CNN can have a significant number of parameters, particularly for large pictures or deep networks.