## 6 Discussion  
Experimental results show that Vuforia and MAXST outperform ARCore. MAXST shows the best performance in terms of the recognizable distance, while Vuforia is the second best. In terms of the recognizable viewing angle, they all show similar performance. For the robustness to occlusion, Vuforia is better than others, while MAXST and ARCore are similar. MAXST and ARCore are limited in terms of the number of targets that can be recognized simultaneously; only three and one can be recognized by MAXST and ARcore, respectively. Vuforia can recognize and track more targets simultaneously without such a limitation. Overall, we consider that Vuforia is relatively superior to the others. In addition, we find issues that should be considered to realize the target application scenario in a real store environment. First, products that are similar or identical in product packaging but differ in size might not be correctly recognized. Second, due to the nature of the environment in which many products are displayed closely, the AR contents of the products next to each other may overlap with each other, or the content of one product may hide the other products. Third, the structure of the shelves can affect the recognition performance. We below discuss those issues in more detail. _Incorrect Recognition Cases._ While the AR frameworks recognized the target products correctly in most cases, there were some cases in which products with similar packaging images and text were not recognized correctly, and they were recognized as other products. As mentioned earlier, the front images of product packages are used as target images to be recognized by the AR frameworks. If the feature points extracted from the images are similar, the products might not be correctly identified. In the case of the cup-type product, there is a problem that the same product with different cup sizes cannot be distinguished. There are cup-type products available in two sizes: large and small. In this case, the image of the cup package is the same regardless of the cup size. These issues may harm the user experience when a user uses a mobile AR application in a real environment, such as a grocery \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distance** & **\# of Products** & **Seen by Camera** & **\# of Distinct Products** & **MAXST** & **Vuforia** \\ \hline
100 cm & 32 & 8 & 0 & 0 \\ \hline
60 cm & 15 & 3 & 1 & 1 \\ \hline
35 cm & 6 & 2 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The number of cup-type products simultaneously recognized and tracked in the store. store. Thus, a solution to the incorrect recognition problem is needed to distinguish a number of similar products and display AR content correctly. Overlap between AR contents or between AR contents and productsAs discussed above, there is an issue that the AR contents shown over the adjacent products may overlap with each other when multiple neighboring products are recognized at the same time. Accordingly, some contents can be occluded by others and cannot be seen by users. In addition, there may be a situation in which the AR content over one product hides another adjacent product, making it difficult for a user to identify the product on the display. Usually, a large number of products are displayed closely in the grocery store environment. It is likely that multiple AR contents are displayed at the same time, and a user may be confused about which content corresponds to which product. To solve this problem, the AR content can be made smaller so that it is displayed only over the corresponding product area, not covering other products. In this case, however, the content might be too small for a user to see. Thus, it is necessary to appropriately design the AR content and application UI in consideration of the amount and characteristics of information to be provided so that the user experience is not compromised.