Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset

Abstract

Shoplifting poses a significant challenge for shop owners as well as other stakeholders, including law enforcement agencies. In recent years, the task of shoplifting detection has gained the interest of researchers due to video surveillance generating vast quantities of data that cannot be processed in real-time by human staff. In previous studies, different datasets and methods have been developed for the task of shoplifting detection. However, there is a lack of a large benchmark dataset containing different behaviors of shoplifting and standard methods for the task of shoplifting detection. To overcome this limitation, in this study, a large benchmark dataset has been developed, having 900 instances with 450 cases of shoplifting and 450 of non-shoplifting with manual annotation based on five different ways of shoplifting. Moreover, a method for the detection of shoplifting is proposed for evaluating the developed dataset. The dataset is also evaluated with methods as baseline methods, including 2D CNN and 3D CNN. Our proposed method, which is a combination of Inception V3 and BILSTM, outperforms all baseline methods with 81 % accuracy. The developed dataset will be publicly available to foster in various areas related to human activity recognition. These areas encompass the development of systems for detecting behaviors such as robbery, identifying human movements, enhancing safety measures, and detecting instances of theft.

Keywords

• bidirectional long short-term memory
• convolutional neural network
• human activity recognition
• shoplifting detection