# Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
## 2 Related Work  
This section will introduce current datasets and methods utilized for the detection of shoplifting. [PERSON] et al. [14] conducted a study on categorizing instances of shoplifting using the Jubatus plug-in to extract feature values from images in order to analyze unusual customer behavior. A linear classifier and kNN classifier were used to classify surveillance video data in the proposed application and calculate the probability of shoplifting. [PERSON] et al. [15] developed an algorithm for detecting violence and theft in video surveillance. Their approach involved dividing the frame into eight regions and detecting changes in the speed of the person being monitored. [PERSON] et al. [16] proposed a method for detecting anomalies using a pre-trained C3D model to extract features and a full-link neural network for regression. They used the UCF-Crime dataset [3] to train their model on 11 classes and tested its performance on videos depicting theft, fights, and traffic incidents. [PERSON] et al. [9] introduced a real-time anomaly detection approach for identifying 13 anomalous behaviors, including theft, burglary, fighting, shooting, and vandalism. They utilized a 3D CNN network for feature extraction and categorization of the samples into normal and abnormal classes. Their approach included a rating loss function and a fully connected neural network for decision-making, which was trained using the labeled data. The effectiveness of a neural network model on a dataset of real-time shoplifting videos was analyzed in [11] using a 3D CNN for feature extraction and classification. [PERSON] et al. [13] present an intelligent video surveillance system designed to detect shoplifting activities in retail stores. The system utilizes a convolutions neural network (CNN) to analyze the typical features of motion and appearance of a shoplifter and a deep learning module based on long-term memory (LSTM). The system identifies the shoplifting activities by analyzing the appearance and motion features in a video sequence. The performance of the proposed system is evaluated on a dataset of 177 videos, with 87 shoplifting cases, and showed promising results with accuracy = 90%. [PERSON] et al. [3] present a novel approach for detecting shoplifting using a hybrid network that combines a 3D convolutional neural network (CNN) and gated recurrent units (GRU). The model uses the spatiotemporal features extracted from a video sequence by the 3D CNN and GRU, then uses the network to classify the actions as either normal or shoplifting. The approach is evaluated on a subset of the UCF crime dataset [8] of real-life shoplifting videos; UCF consists of 28 videos of shoplifting. The dataset was augmented artificially by dividing each video into 32 video segments. Due to the limited size of the dataset, which only contained 310 instances, it was deemed necessary to artificially increase its size. The approach taken involved horizontally mirroring each video fragment, which resulted in 620 instances. The results show improved accuracy with 93 % compared to other methods. In previous studies, the majority of efforts have been focused on the task of shoplifting detection based on human activity recognition problems [3; 13]. The task has been explored with 3D CNN [9; 10; 11], CNN + GRU [3], and CNN + LSTM [13]. In addition, the task was explored over very small datasets containing only 155 videos of shoplifting, and another containing a total of 175 video clips, with 88 clips depicting normal behavior and the remaining 87 clips showing instances of shoplifting [13], UCF-Crime dataset [3], including a subset UCF crime consisting of 28 videos recorded from surveillance cameras in retail stores. The major limitations of the existing datasets are as follows. First, the size of these datasets is small. Second, the corpora have only limited cases of shoplifting. We need a large and highly qualitative dataset to develop an efficient shoplifting detection system.