# Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
## 4 Methods
### Proposed Method  
CNN has proven to be a highly effective method for image classification, outperforming other approaches. When it comes to video classification, the features of each frame in a sequence need to be extracted and inputted into a Bidirectional Long Short-Term Memory. Since CNN can identify hidden patterns in individual frames and changes in a sequence of frames, it is the best option for feature extraction. However, training a CNN from scratch requires a large dataset of images, substantial computational resources, and significant time investment for training and testing the model, making it a costly approach. Transfer learning can be used to mitigate this problem, where the last hidden layers of a pre-trained CNN can be removed, and only the image features are extracted. The reason for selecting the Inception model is a convolutional neural network (CNN) architecture known for its ability to capture both local and global features from images effectively. It utilizes a combination of parallel convolutional filters of different sizes, enabling it to capture details at various scales. Shoplifting detection often involves analyzing video frames or images to identify suspicious activities or objects. The Inception model's robust feature extraction capabilities can be leveraged to capture relevant visual cues, patterns, and object characteristics. By using the Inception model as the visual backbone, the hybrid system can effectively process and understand the visual information from the surveillance footage. In addition, the Inception model has been used and showed exceptional performance for human activity recognition tasks [2; 3; 12; 28]. Based on this, the method is proposed. The detailed architecture is shown in Figure 7. In the first step, the features from the frames were extracted using Inception V3 [31] with \(n\) = 7 for the 17 \(\times\) 17 grid and 2 \(\times\) Inception. We choose \(n\) = 7 for the 17 \(\times\) 17 grid, and 2 \(\times\) 2 Inception represents the module with expanded filter bank outputs. Sixteen convolution layers were used, and the final dense layer was removed. The working of inception V3 is shown in Figure 8. We just removed the last dense layer and fed the features into BILSTM [32]. Figure 7: Proposed architecture. Bidirectional Long Short-Term Memory (BIL-STM) and Inception V3 are the two components of our proposed method, as shown in Figure 7. For the feature extraction of the frames in the proposed technique, we used parameters of a trained CNN dubbed Inception V3, which was trained on ImageNet [33] with 1.2 million training pictures, 50,000 validation images, and 100,000 testing images. Inception V3 is a deep convolutional neural network (CNN) architecture that was proposed by Google in 2015 as an improvement over the original Inception architecture [31]. In the initial Inception design, 5 \(\times\) 5 convolution filters, which were computationally costly and produced overfitting, were utilized. These filters are replaced in Inception V3 with a mixture of 3 \(\times\) 3 and 1 \(\times\) 1 filters, which are more computationally effective and aid in reducing overfitting. Inception V3 starts the network with a stem module made up of pooling layers, 3 \(\times\) 3 and 5 \(\times\) 5 convolution filters, and other components. This aids in capturing characteristics at various scales and enhances the network's capacity to identify objects of various sizes. After each grid of inception modules, Inception V3 employs a reduction module to lower the dimensionality of the feature mappings. This is accomplished by combining 1 \(\times\) 1 convolution filters with max pooling, which lowers the number of parameters in the network and increases its effectiveness. After the first grid of inception modules, Inception V3 employs an auxiliary classifier to assist in regularizing the network and avoid overfitting. This classifier's predictions are mixed with the network's final predictions once it has been trained to predict the output class.