# Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
## 1 Introduction  
Each year goods worth billions of dollars are shoplifted globally [1]. Shoplifting (also known as retail theft) is a criminal act that reduces the profitability of businesses. It involves stealing merchandise from a retail establishment without paying for it while avoiding detection [1, 2]. Shoplifting is a crime that receives significant attention among the various criminal activities that occur in stores. It also involves stealing merchandise from a retail establishment by hiding the items in one's clothing, pockets, or bag and leaving the store without paying for them. Shoplifting poses a significant challenge for store owners and other entities such as law enforcement, the government, and the justice system. A study conducted by the National Association for Shoplifting Prevention indicates that one out of every 11 individuals engages in shoplifting. Additionally, it has been reported that these thieves are apprehended only once in every 48 instances of theft [1].  
There are several ways to shoplift, but the majority of them involve hiding products and exiting the business without paying. The act of \"hiding,\" which is followed by a particular series of behaviors, is what ties these strategies together. This behavior is viewed as aberrant from the standpoint of the regular shopper. Reviewing surveillance footage may make it simple to spot instances of stealing, but as the number of customers rises, itbecomes more difficult to keep track of everyone [3]. This leads to a persistent issue, which is made worse by the fact that there are more shoplifters every year. Certain countries have implemented specific laws to address shoplifting and penalize offenders accordingly. In India, shoplifting is considered an offense against property and is dealt with under Chapter XVII of the Indian Penal Code (IPC) [4]. Section 379 of the IPC stipulates that those found guilty of theft can face imprisonment for up to three years, a fine, or both. Retailers have started using closed-circuit television (CCTV) surveillance systems to monitor customers in stores [5; 6], but the real-time analysis of footage is a challenging task for humans to undertake continuously. As a result, an advanced monitoring system that analyzes human behavior and identifies shoplifting incidents may be the most effective solution. In an effort to improve public safety, more and more video surveillance cameras are being deployed in public spaces, including streets, banks, shopping centers, and retail stores, every year. Security professionals are unable to handle the enormous volumes of data produced by these camera networks in real-time. The abundance of recording devices has made monitoring a more difficult task [7].  
Prior research has primarily concentrated on identifying shoplifting by analyzing human behavior using methods such as 2D CNN [8], 3D CNN [9; 10; 11; 12], RNN [3], and LSTM [13]. Previous studies on shoplifting detection have some limitations, such as training on the small size of the dataset and limited cases of shoplifting. For example, one dataset [3] contained only 155 shoplifting videos, while another had a total of 175 video clips, with 88 clips showing normal behavior and 87 clips depicting instances of shoplifting. Another dataset, UCF-Crime [3], included a subset called UCF crime with 28 videos recorded from retail store surveillance cameras. Moreover, none of these datasets include cases of shoplifting where individuals are asked to commit theft, which would reflect how an average person might attempt to steal. As a result, a larger and higher-quality dataset is needed to develop an effective shoplifting detection system.