## 5 Real Store Experiment
### Results  
#### 5.2.1 Performance Depending on the Distances  
In the case of box-type products, both MAXST and Vuforia show similar performance, except at a distance of 1 m, as shown in Table 3. At a distance of 1 m, a total of 16 products are caught on the camera, but there are about 6 different target products among them. This is because many of the same products are arranged in succession. The application using MAXST recognizes up to three products, while one with Vuforia does not recognize any products because the distance is beyond its maximum recognizable distance. In contrast, at 60 cm and 50 cm distance, MAXST and Vuforia recognize one or two different target products when about 5 products are viewed by the camera. We find that sometimes the AR content that does not match the product is shown when scanning products while moving a smartphone. This happens when the images of product packages are similar. Such a wrong recognition occurs relatively more when using MAXST than Vuforia.  
In the case of pouch-type products, MAXST and Vuforia show similar trends (see Table 4). At 1 m distance, a total of eight different target products are viewed. In most cases, they are not well recognized by MAXST, and sometimes only one product is recognized. At 60 cm and 50 cm, two or three different target products are visible and all of them are recognized. On the other hand, Vuforia does not recognize products at a distance of 1 m, but it does recognize two or three different target products at 60 cm and 50 cm. Similar to the box-type product, there are some cases in which both AR frameworks sometimes misidentify the products and display the AR content of other products.  
Both AR frameworks also show similar performance for the cup-type products, but the performance is relatively low compared to other products, as shown in Table 5. This is mainly because the package of the cup-type products has a curved surface, and the target images are relatively small  
\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 & 16 & 8 & 0–1 & 0 \\ \hline
60 cm & 5 & 2–3 & 2–3 & 2–3 \\ \hline
50 cm & 4–5 & 2–3 & 2–3 & 2–3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The number of pouch-type products simultaneously recognized and tracked in the store.  
\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 & 16 & 6 & 3 & 0 \\ \hline
60 cm & 5 & 1–2 & 1–2 & 1–2 \\ \hline
50 cm & 4–5 & 1–2 & 1–2 & 1–2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of box-type products simultaneously recognized and tracked in the store.  
in size. At 1 m distance, neither framework recognizes any of the products. At 60 cm distance, a total of three different target products are viewed by the camera, but only one product is recognized by both frameworks. At 35 cm distance, two different target products are visible and both of them are recognized by the two frameworks. Similarly to the other products, the problem of misrecognizing similar products occasionally occurs.