## 4 Experimental Results  
The proposed full pipeline has been tested in a real market store, but each of the designed subsystems have been validated both using a custom dataset as well as through a public database. We have validated the \"intelligent core\" of the GAIA as follows. The first subsystem we have validated is the computer vision subsystem as per the pipeline reported in Figure 4. Specifically, we have tested the performance of our proposed modified version of YOLOv5 small. The used enhanced YOLOv5 small is configured as follows: input frame dimension (227 \(\times\) 227 \(\times\) 3), N-Anchors: 6; learning rate: 0.001; Batch-size: 8; L2 regularization factor: 0.0005; penalty threshold: 0.5, dropout enabled. The used sensing camera is a classical low framerate (60 fps), high-definition resolution and high dynamic range. The calibration parameters of the 800 \(\times\) 600 tensor \(Mp\) were determined by the genetic algorithm as per Equation (3), while the parameter \(T\) was fixed heuristically (after several tests) to 1.55. The input frame will be resized via a bicubic algorithm. The dataset is composed of parts from [44] and is partly generated locally in the tested market stores. The deep network was trained by the SGDM algorithm, while the dataset was split as 70% for training, while the remaining 15% was for validation and 15% for testing. A k-fold (\(k\) = 5) cross-validation approach was used. The following Table 3 reports the cross-validated test set benchmarks compared with other classical deep networks. Regarding the customer sentiment analysis subsystem, we have tested the proposed Mask-R-CNN embedding a ResNet-101 with Criss-Cross backbone. The used configuration and learning hyperparameters are the following: input frame dimension (227 \(\times\) 227 \(\times\) 3); learning rate: 0.001; batch size: 16; L2 regularization factor: 0.0002; penalty threshold: 0.5, dropout enabled. As the downstream classifier of the tracked customer-skeleton junction points time series, we implemented a convolutional LSTM. Specifically, we have designed an input layer of 16 \(\times\) 224 (as described in the relevant section of this paper). For the convolutive blocks, we opted for a backbone based on VGG19 embedding the convolutional and activation layers up to the fully connected (excluded) stacks of this architecture. In the unfolding and flattened sequence block, we have adapted the dimensions with further convolutional blocks and therefore passed the so-computed features to a deep LSTM composed of six hidden layers of 100 vanilla LSTM basic vanilla units (see Figure 9 for unit structure). The LSTM cell is able to select which information to discard or store. In order to produce effective results in real applications, this selective method requires three different mechanisms to read, store and discard information by taking advantage of specific selectors called \"gates\". Basically, the \"input gate\", \"output gate\" and \"forget gate\" processing is implemented via activation functions suitable to define if such information is relevant or not. More in detail, given \(x_{t}\) as the input vector, \(h_{t-1}\) as the previous cell output, \(C_{t-1}\) as the previous cell memory, \(h_{t}\) as the current cell output and \(C_{t}\) as the current cell memory, we defined Equations (9)-(11) to determine which information to store. Finally, we generated the output of LSTM by updating the old cell state as per Equation (12) and merging the previous output, input and bias vector of Equations (13) and (14). \[f_{t}=\sigma(W_{f}\cdot[h_{t-1},\ x_{t}]+b_{f} \tag{9}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Deep Network** & **AP (Average Precision)** & **mIoU** \\ \hline Faster-R-CNN & 93.75\% & 80.09\% \\ (ResNet backbone) & & \\ \hline Mask-R-CNN & 93.91\% & 83.22\% \\ (DenseNet backbone) & & \\ \hline YOLOv5 & 94.05\% & 83.17\% \\ \hline Proposed & **95.33\%** & **85.07\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computer vision subsystem (enhanced YOLOv5 small) benchmark comparison.