## 5 Data and Results
### Final Results  
Once the confidence intervals for both visible-light and thermal infrared spectra have been fixed through the experimental assignment of the confidence threshold values (see Figure 1), we summarize the segmentation results in Table 4 with the objective of highlighting which spectrum is suited better for the environmental conditions of the recorded sequences. The \(-2^{\circ}\)_Fog_ and \(8^{\circ}\)_Night_ sequences show better performance in the thermal infrared spectrum, whilst the _15\({}^{\circ}\)Cloudy_ and _28\({}^{\circ}\)Sunny_ sequences work better in the visible-light spectrum. Lastly, sequence _3\({}^{\circ}\)Sunny_ shows similar results for both spectra. Now, we are going to explain the results obtained for each sequence by providing some qualitative examples shown in Figure 7. Results for the sequence \(-2^{\circ}\)_Fog_ show problems in the visible spectrum when humans are close to the building in the scene background. There is a greater fog concentration in that zone, as shown in Figure 7a. At the same time, humans close to the camera are not easy to detect, because scene colors appear more attenuated, due to the fog, and humans are harder to detect. These problems were predictable due to the illumination features of the frames composing this sequence. On the other hand, human detection in the infrared spectrum barely has any problems, and false negatives only appear on a few occasions. In Figure 7a, it can be appreciated that the human is detected without any problem in the infrared spectrum. The results confirm the confidence value \(HIGH\) in the thermal infrared spectrum. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequence** & **Spectrum** & **Recall** & **Precision** & \(F\) & **Confidence Value** \\ \hline \multirow{2}{*}{\(-2^{\circ}\)Fog} & Visible & 0.52 & 1.00 & 0.68 & \(LOW\) \\  & Infrared & 0.95 & 0.95 & 0.95 & **HIGH** \\ \hline \multirow{2}{*}{\(8^{\circ}\)Night} & Visible & 0.08 & 1.00 & 0.15 & \(LOW\) \\  & Infrared & 0.81 & 0.86 & 0.83 & **MEDIUM** \\ \hline \multirow{2}{*}{\(3^{\circ}\)Sunny} & Visible & 0.93 & 0.96 & 0.95 & **HIGH** \\  & Infrared & 0.98 & 0.91 & 0.94 & **HIGH** \\ \hline \multirow{2}{*}{\(15^{\circ}\)Cloudy} & Visible & 0.97 & 0.98 & 0.97 & **HIGH** \\  & Infrared & 0.91 & 0.97 & 0.94 & \(MEDIUM\) \\ \hline \multirow{2}{*}{\(28^{\circ}\)Sunny} & Visible & 0.81 & 0.97 & 0.88 & **HIGH** \\  & Infrared & 0.39 & 0.96 & 0.55 & \(LOW\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Final results achieved for each sequence in both spectra. Sequence _8\({}^{\circ}\)Night_ is especially difficult for the different algorithms, because it was recorded in the early night hours. At that time, buildings have not yet cooled, and their thermal readings are still high compared to the environment, causing them to appear at almost the same temperature as the humans in the scene. However, human detection in the thermal infrared spectrum works well when people are far from the background, as shown in Figure 7b. Though, problems can appear when people approach the buildings and the human body temperature is similar to the thermal readings of the building in the background. These factors cause the confidence on the infrared spectrum to be set as \(MEDIUM\). On the other hand, the visible-light camera is blinded, since the scene was recorded in night conditions. An example of this situation in the visible spectrum is shown in Figure 7b. Humans can only be detected  
Figure 7: Results obtained on both spectra for the analyzed sequences. (**a**) Sequence \(-2^{\circ}\)_Fog_; (**b**) sequence _8\({}^{\circ}\)Night_; (**c**) sequence _3\({}^{\circ}\)Sunny_; (**d**) sequence _15\({}^{\circ}\)Cloudy_; (**e**) sequence _23\({}^{\circ}\)Sunny_. when they are near the lamp post lighting the scene in one of the scenario sides. Because of this, the confidence of the visible spectrum is set as \(LOW\). Results for both spectra are great in the sequence _3\({}^{\circ}\)Sunny_.