# Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
## 4 Methods
### Baseline Methods
#### 4.1.2 Three-Dimensional Convolutional Neural Network  
A sort of neural network called a 3D CNN (Convolutional Neural Network) is capable of processing three-dimensional data, such as movies or 3D photographs. An expansion of a 2D CNN, a 3D CNN convolves each filter across the input volume as opposed to a 2D picture. A 3D CNN may be utilized for a variety of tasks, including robotics [26], medical imaging, and action identification in films. A 3D CNN may identify an action by analyzing the spatio-temporal aspects of a video stream in the instance of action recognition [27]. The three-dimensional data that are sent into the network for processing make up the input layer of the network. The main components of a 3D CNN are its 3D convolutional layers. A series of learnable filters make up each 3D convolutional layer, which convolves across the input volume to produce a set of feature maps. An activation layer is often added to the network to add non-linearity after each 3D convolutional layer.  
The feature maps produced by the 3D convolutional layers are down-sampled using 3D pooling layers. In order to conduct classification based on the output of the preceding layers, the fully linked layers are often added toward the end of the network. The network's output layer generates the final prediction or output. In general, a 3D CNN's structure may be modified to meet the unique needs of a particular issue, and the number and kinds of layers utilized in the network can change based on how difficult the problem is being addressed.  
In our case, we have utilized a 3D model to train a dataset that identifies shoplifting. Our training set consists of 81,000 images with (frame sequence = 90) for each movie chosen for training input out of a total of 900 videos from 2 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, 8 Convolutional layers were used to extract low-level features for learning from the sequence of frames. After each 3D convolutional layer, an activation layer was added to the network image with the RELU activation function [3,28]. In the final step, the softmax activation function [23,24] was used for the final prediction.  
The incorporation of an extra-temporal dimension in 3D CNNs raises the training and inference computing costs. Due to this, training big 3D CNNs on hardware with limited resources might be difficult. Since volumetric data is processed using 3D CNNs, a sizable amount of memory is needed to store the data and intermediate feature maps [29]. The amount of input data that may be processed may be constrained as a result. While photos are plentiful and simple to get, 3D video data are still uncommon in many applications.  
This can impair the performance of 3D CNNs by limiting the supply of big, high-quality training datasets. It can be difficult to evaluate the learned characteristics and comprehend how they contribute to the network's predictions because of the increasing complexity of 3D CNNs. This may reduce the 3D CNNs' transparency and interpretability, which may be a problem in some applications. In order to extract useful characteristics, 3D CNNs frequently need spatial context, which might be challenging to get in particular applications. The performance of 3D CNNs may be constrained in specific medical imaging applications, such as brain tumor segmentation, where the spatial context may not be present. Like any other deep learning model, 3D CNNs are susceptible to over-fitting, especially when the dataset is small [30].