## 4 Artwork Recognition in a Real Environment
### Dataset  
For our experiments, we chose a specific hall in the State Gallery which hosts 16 artworks in total, including 13 paintings and 3 sculptures (shown in Figure 5). A first-person \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Experiment 1:**} & \multicolumn{2}{c}{**Experiment 2:**} \\  & & **Correct Classification Rate** & **Correct Classification Rate** \\ \hline \multirow{4}{*}{Wall texture} & No texture (baseline) & 99\% & 100\% \\ \cline{2-4}  & Green stripe & 95\% & 100\% \\ \cline{2-4}  & Blue stripe & 94\% & 98\% \\ \cline{2-4}  & Gold fan & 88\% & 100\% \\ \hline \multirow{4}{*}{Number of visitors} & 0 (baseline) & 99\% & 100\% \\ \cline{2-4}  & 5 & 92\% & 100\% \\ \cline{2-4}  & 10 & 71\% & 98\% \\ \cline{2-4}  & 15 & 83\% & 93\% \\ \hline \multirow{4}{*}{Distance} & 150 cm (baseline) & 99\% & 100\% \\ \cline{2-4}  & 200 cm & 88\% & 100\% \\ \cline{2-4}  & 250 cm & 78\% & 100\% \\ \cline{2-4}  & 300 cm & 76\% & 100\% \\ \hline \multirow{4}{*}{Camera point of view} & Forward (baseline) & 99\% & 100\% \\ \cline{2-4}  & Upwards & 98\% & 100\% \\ \cline{2-4}  & Downwards & 77\% & 100\% \\ \cline{2-4}  & Left & 87\% & 100\% \\ \cline{2-4}  & Right & 86\% & 100\% \\ \hline \multirow{4}{*}{Ambient light intensity} & 255 (baseline) & 99\% & 100\% \\ \cline{2-4}  & 200 & 96\% & 100\% \\ \cline{2-4}  & 150 & 63\% & 99\% \\ \cline{2-4}  & 100 & 27\% & 46\% \\ \hline \multirow{4}{*}{Camera speed} & 10 s & 100\% & 100\% \\ \cline{2-4}  & 20 s (baseline) & 99\% & 100\% \\ \cline{1-1} \cline{2-4}  & 30 s & 99\% & 100\% \\ \cline{1-1} \cline{2-4}  & 40 s & 99\% & 100\% \\ \hline Random camera movements & N/A & 72\% & 98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recognition rates for different simulated conditions. dataset consisting of two subsets was created, using a midrange smartphone camera, to be realistic, on regular visitors. Following the outcomes of the experiments in the virtual environment, two subsets of training images were created. The first subset contained 500 images for each artwork captured from different points of view and distances. In addition, it included 500 images for the \"No Artwork\" category covering the floor, ceiling and hall's walls. In total, the first subset contained 8500 images and was used for the training of different CNN models capable of recognizing the hall's artworks. The second subset was used for further testing the recognition accuracy of the best performing CNN model of the previous step. It contained 750 images per artwork captured from three different distances (1 m, 1.5 m and 2.5 m), using five different camera points of view (forward, downwards, upwards, left and right), representing 15 combinations in total. Fifty images were collected for each combination, resulting in 12,000 images (15 combinations \(\times\) 16 artworks \(\times\) 50 images) (\(720\times 1280\) resolution). The subset was expanded to include changes in ambient light intensity and occlusions after processing at image level using the Python Pillow library [29]. In the case of light intensity, the brightness level of all images was modified from 255 to 200, 150, 100 and 50 (Figure 6). Similarly, human silhouettes were added as occlusions between the visitor and artwork at various percentages (e.g., 50-90%) of the silhouette's height in relation to the height of the image, as shown in Figure 7.  
Figure 5: The artworks used in the experimental setup (artworks marked with an * are 3D objects). Qualitative and quantitative training data significantly improve the performance of deep learning algorithms. The availability of annotated datasets is an important step in the development of first-person algorithms. Although there are several ways that can facilitate the collection and annotation process, creating a first-person dataset remains a difficult and time-consuming process due to the large amount of data and rich visual content.