## 1 Introduction  
Augmented Reality (AR) technology provides a new way of information visualization and interaction with physical/virtual objects [1; 2; 3]. It has been used in various application domains such as retailing [4; 5; 6], manufacturing [7; 8], tourism [9; 10], education [11], and entertainment [12; 13]. In particular, it has high potential to change the paradigm of consumer behaviors in the retail and marketing fields because it can enable real-time interactive communication with consumers. In the case of fast-moving consumer goods (FMCGs), such as fashion, food, and cosme, which have fast consumption cycles, real-time communication with consumers can be a key factor in sales. Accordingly, the importance of interaction marketing based on AR technology will increase [14]. Recently, keywords, such as healthy foods and home meal replacement (HMR), have been leading the food market [15]. In such a circumstance, AR technology can have a significant impact on consumers' food choice behavior. For example, at a grocery store, consumers can use an AR application on their smartphones that helps them buy food for a healthier diet, allowing them to intuitively check nutritional information or ingredients.  
A number of mobile AR frameworks are now available and used to support the development of diverse mobile AR applications, including Google's ARCore, Apple's ARKit, PTC's Vuforia, and so on. These AR frameworks commonly provide a set of APIs (Application Programming Interfaces) that developers can use to create AR applications. They also provide key functionalities to recognize 2D/3D physical objects and render virtual content over the objects on the mobile screen. However,there are differences in the details of their features and performance. Such differences may cause differences in the function or performance of the applications developed using the AR frameworks. To provide users with a useful and easy-to-use application, it is important to choose an appropriate AR framework that can meet the purpose of the application. Although previous studies have compared the functions of diverse AR frameworks [16; 17; 18; 19], no research has been conducted to actually measure and compare their performance. Moreover, there has been little attention paid to whether commercial AR frameworks can provide sufficient support in the aforementioned AR-based food marketing domain or whether they have potential technical issues that we need to address for the application domain.  
In this paper, we measure and compare the performance of commercial AR frameworks to provide helpful information for the development of mobile AR applications. Because the performance measurement should be performed under different workload conditions depending on the type of application to be used, we select one application domain and prepare an appropriate setup for our experiment. As an application domain for the experiment, we assume a situation in which a consumer purchases food products at a place, such as a grocery store, and we consider an application scenario in which AR content related to the products is displayed on a smartphone screen by recognizing the products.  
For comparison, we use three well-known commercial AR frameworks (i.e., Vuforia, ARCore, and MAXST) that support the development of mobile AR applications. Vuforia and ARCore are the world's well-known AR frameworks that are widely used, while MAXST is the AR framework provided by a leading AR software vendor in Korea. There are many aspects of the performance, but the most important is recognizing target products correctly. In this paper, we conduct experiments to measure and compare the target recognition performance from four different dimensions: distance, viewing angle, occlusion, and simultaneous recognition.