# Using Deep Learning for Collecting Data about Museum Visitor Behavior
## 4 Experimental Results
### Performance Analysis
This penalizes systems capable of achieving high Precision only in retrieving a subset of objects. Normally, this particular curve is used to compare one system to another, but when it comes to performance analysis, it shows how a system is performing when its parameters are changed. As mentioned above, there is another type of curve known as _Recall-IoU curves_, which are the basis of another metric used to evaluate the performance of a detector, namely, the _Average Recall (AR)_[55]. Such curves are obtained by plotting the Recall values corresponding to the _IoU_ values \(\in[0.5,1.0]\). The Average Recall is defined as the Recall averaged over all _IoU_ values \(\in[0.5,1.0]\). It can be calculated as twice the area under the Recall-_IoU_ curve:  
\[AR=2\int_{0.5}^{1}Recall(o)do \tag{8}\]  
where \(o\) is _IoU_ and \(Recall(o)\) is the corresponding value of Recall. There exist several variants of the metrics above. Among the others,  
* \(AP@IoU\)=0.50: 5:95 is the \(AP\) value averaged over 10 different _IoU_ threshold values (i.e., 0.50, 0.55, 0.60,..., 0.95). Furthermore, there is also Average Precision calculated for different object scales. So, we have:  
* \(AP@\) area = small, which represents AP for objects that cover an area less than \(32^{2}\) pixels;
* \(AP@\) area = medium, which represents AP for objects that cover an area higher than \(32^{2}\) pixels but lower than \(96^{2}\) pixels;
* \(AP@\) area = large, which represents AP for objects that cover an area higher than \(96^{2}\) pixels;
* \(AP@\) area = all, which represents AP for objects of any size. The area is given by the number of pixels present in the segmentation mask. Finally, we have AP calculated for different detection numbers per image, defined as follows:  
* \(AP@\) maxDets = 1, which represents AP given 1 detection per image;
* \(AP@\) maxDets = 10, which represents AP given 10 detections per image;* \(AP@\) maxDets = 100, which represents AP given 100 detections per image. The same variants of the Average Precision metric also apply to the Average Recall. Before presenting the experimental results, it is necessary to describe the test set used. To evaluate the performance of our system using the metrics introduced above, we randomly selected 300 images from 10 videos containing a total of 13 object classes (12 specific badges + the face object). Figure 6 shows the values of the Average Precision metric on the test set for our object detector as the number of epochs increases. It can be noted that there are high AP values already with a low number of epochs. We have only reported the value of \(AP@\) maxDets = 100, as the values for maxDets = 1 and maxDets = 10 are the same as above. Furthermore, we have not reported the value of \(AP@\) area = small, because we excluded a priori the detection of badges that are too small, that is, worn by visitors at such a distance from the point of interest that they cannot be considered in its surroundings. Figure 7 shows the trend of the Average Recall values on the test set as the number of epochs increases. Also in this case, the values are already high after a few epochs. The experimental analysis was performed on an NVIDIA QUADRO P2000 GPU capable of analyzing 4 frames per second. This system can, therefore, be used to perform a real-time analysis with fine tuning and optimization of the parameters.