## 4 Artwork Recognition in a Real Environment
### Experimental Results  
The MobileNetV2 recognition model was tested using the second subset, in order to evaluate its performance in real conditions and under the different factors affecting the recognition accuracy. The model was loaded into memory and fed with the images in the dataset one by one, recording its prediction for each case to calculate the total recognition accuracy. The confusion matrix depicted in Figure 8 shows the high recognition accuracy achieved before the changes in brightness intensity and introduction of occlusions. The increased model misclassifications between artworks 5 and 7 indicate a need to increase the examples of these classes during training. The increased misclassifications between artworks 5 and 7 arise because the two artworks are located next to each other in the gallery. Furthermore, artwork 5 is a 3D object (a sculpture) placed in a corner of the gallery hall while artwork 7 is a painting placed close to artwork 5, on the adjacent wall. As a result, in numerous frames showing artwork 5 (depending on the angle of view), parts of artwork 7 are also visible (see Figure 9). Due to the fact that the pattern of artwork 7 is distinct, the chance for misclassifications increases when parts of artwork 7 are shown in combination \begin{table}
\begin{tabular}{l l l} \hline \hline
**InceptionV3** & **MobileNetV2** & **VGG19** \\ \hline InceptionV3 original layers (excl. top layer, initialized with ImageNet weights) & MobileNetV2 original layers (excl. top layer, initialized with ImageNet weights) & VGG19 original layers (excl. top layer, initialized with ImageNet weights) \\ \hline Global average pooling 2D layer & Flatten layer & Flatten layer \\ \hline Dense layer (1024, activation = ReLU) & Dropout layer (rate = 0.5) & Dropout layer (rate = 0.5) \\ \hline Dense layer (17 classes, softmax) & Dense layer (17 classes, softmax) & Dense layer (17 classes, softmax) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The layer configuration used for the training of the CNNs. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Architecture** & **Model Size (.tflite)** & **Inference Time/Frame** & **Accuracy** \\ \hline VGG19 & 78 MB & 4000 ms+ & 0.9739 \\ VGG19 (quantized) & 20 MB & 1800–2000 ms & 0.9749 \\ InceptionV3 & 91 MB & 600–800 ms & 0.9902 \\ InceptionV3 (quantized) & 23 MB & 400–550 ms & 0.9869 \\ MobileNetV2 & 15 MB & 200–350 ms & 0.9967 \\ MobileNetV2 (quantized) & 4 MB & 180–300 ms & 0.8693 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the trained CNNs. with artwork 5. The use of additional training samples of 3D objects, where other artworks also appear in the scene, may enhance the ability of the classifier to classify correctly 3D objects in the presence of visual stimuli from other artworks. As shown in Table 4, the recognition model performs very well in strong ambient light condition and without occlusions between the visitor and artwork with almost 100% accuracy at 1 m and 1.5 m, and 88.5% accuracy at 2.5 m. The majority of the misclassified predictions at 2.5 m are caused by the appearance of more than one artwork in the image which sometimes leads to the classification of an artwork in the adjacent class. The camera point of view does not play a significant role in the recognition with a prediction accuracy ranging between 91% and 98% for all distances. The predictions verify the experiments conducted in the virtual environment in regard to the effects of brightness and the number of visitors who are present at the same time in the hall. As the brightness level changes from 255 to 50, the recognition accuracy decreases for most combinations with the smallest accuracy observed in the case of the left (\(-\)0.8 %) and the highest in the case of the upwards camera view (\(-\)13.9%). Similarly, the accuracy decreases while the height of the human silhouettes increases, achieving 72.6% as the highest value when the height is equal to 50% of the image.