## 6 Results and Discussion
This can generate detection issues related to lighting contrast, which cannot be detected during others seasons of the year, which explains the newly emerged detection issue, since the first test campaign had been performed in May. Therefore, conducting several tests during different periods of the day and year is strongly recommended for camera-based sensor systems. A preferable solution to overcome the lighting contrast issue is modifying the settings of the camera to correct the lighting contrast. * Failure in detecting the users' entrance due to other kind of obstructions such as open doors. Specifically, the doors opening towards the corridor can obstruct the view of the adjacent room entrance, preventing the system from registering users entering the room (Figure 11). The issue can only be managed by adding new cameras to cover the unexpected blind spots. The issue was unexpected, since doors are usually kept closed when offices are occupied. * Issues in the recognition of cleaning service company employees. The system struggled in detecting the workers due to the presence of the cleaning trolley, which impeded a complete view of the operator. The cleaning trolley provoked an incorrect counting of entries, exits, and occupancy of the rooms. To overcome the issue, the recognition algorithm can be optimized and trained to correctly recognize the cleaning service operator, by recognizing the cleaning trolley. Figure 12 presents the percentages of error types detected during the two test campaigns. During the first test campaign, 30 out of 70 monitored rooms presented detection  
Figure 11: Unexpected blind zone generated by unusual occupants’ behavior. faults, while during the second test campaign, 38 rooms presented detection issues. The reason of the higher number of faults during the second test campaign is mainly due to the lighting issues that emerged only during the second test campaign. During the first test campaign, 70% of errors were related to technical issues: 17% of errors due to not-working cameras, and 53% of errors due to difficulty detecting users in areas too far from the camera-based sensors. As shown in Figure 12, these types of technical issues were completely fixed by adjusting the system settings. Once all cameras were properly working and correctly set, those issues did not occur in subsequent analyses. Another type of issue detected in the first test campaign was related to unexpected user behavior, resulting in 30% of errors. These errors could be adjusted with some improvements in the system. However, a 3% of errors due to unexpected user behavior occurred also during the second test campaign. Despite the error percentage being significantly lower in the second test campaign, this type of error could not be completely avoided because of the unpredictable nature and high variability of user behavior. Regarding the second test campaign, elevated lighting contrasts between different areas of corridors caused 68% of errors. This kind of error was never detected during the first test campaign because of the different period of the year when the test was performed, a key aspect for proper calibration of a camera-based sensors system. The remaining 30% of errors of the second test campaign were related to difficulties detecting the cleaning employee (18%) and to obstructions and obstacles that impeded the detection (11%). Cleaning employee detection issues were classified as a technical error in the first campaign. After the resolution of technical issues, the error persisted, and the actual cause was identified, highlighting the importance of a multi-stage testing of the  
Figure 12: Charts of errors detected during first and second test campaigns. system. Detection issues related to obstacles and obstructions appeared because some pieces of furniture were moved or spaces were reorganized. The frequent check of the correct functioning of the system overtime is fundamental to verify newly appeared issues and consequently adjust and improve the system.