## 4 Results  
After analyzing multiple samples from each of the recorded videos, it was deduced that the best results were obtained in the nighttime scenario, followed by the indoor scenario, and finally by the daytime outdoor scenario. However, the detection rate does not follow a linear relationship with the ambient light level. This will be explained in the following paragraphs and can be observed in Table 3, where the detection percentages of each tested scenario are listed. Figure 7: Procedure phases. In most indoor cases, the undetected transmitter was the same and corresponded to the one with the highest luminous power: the green LED. This was due to its saturation of the image sensor, which prevented it from dropping low enough during the OFF intervals. Consequently, due to the threshold used, its frame was read as a consecutive sequence of bits set to 1. This occurred more frequently during the average exposure time because Equation (3) adjusted the threshold less effectively in this case. On the other hand, in the daytime indoor scenario, in a significant number of cases, half of the transmitters were not detected, which was also due to the differences in luminosity among them. Specifically, the blue LEDs had high luminous power, while the red ones had low power. As a result, the OFF intervals of the LEDs with higher power had the same power as the ON intervals of those with lower power. This caused only half of them to be detected with the same sensitivity threshold. In the remaining cases, a very small percentage of the total positive omissions occurred when one or two transmitters were not detected due to some moving obstructive element. Regarding the false positive rate, the only scenario where they were detected was in the indoor scenario. In this scenario, there were almost as many false positives as true positives. However, upon examining the optical signals of these false positives, it was found that they were not false positives per se but rather reflections of the transmitters on reflective surfaces that were mistakenly identified as transmitters. For example, in Figure 8, the little differences between the data frame received directly from the transmitter (upper) and from a reflection point on the ground (lower) are shown with the most significant distinction being the level of received power. In many instances, information from the omitted transmitter was obtained through its reflection. Therefore, in practical applications, these reflections can be used to improve the result reliability and maintain link continuity. With respect to processing times, the nighttime scenario again yielded the best results (average of 48 ms), which was followed by daytime outdoor (average of 600 ms) and indoor (average of 680 ms). The significant difference in processing times between the first scenario and the other two is due to the substantial difference in ambient light and  
Figure 8: Data frame received directly from the transmitter (**a**) and from a reflection point on the ground (**b**). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Transmitters Detected**} \\ \cline{2-4}
**Scenario** & **2/4** & **3/4** & **4/4** \\ \hline Indoor, low luminosity & 0\% & 29\% & 71\% \\ Indoor, medium luminosity & 14\% & 57\% & 29\% \\ Indoor, high luminosity & 0\% & 21\% & 79\% \\ Daytime outdoor, low luminosity & 82\% & 18\% & 0\% \\ Daytime outdoor, high luminosity & 0\% & 45\% & 55\% \\ Nighttime outdoor & 0\% & 0\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection rates summary. reflective surfaces. A greater number of these parameters resulted in more candidates exiting the simplification phase of the algorithm, requiring a greater number of pattern search operations. The correlation phase was the heaviest part of the algorithm, accounting for 85% of the processing time in the worst scenarios.