## 6 Conclusions  
This paper has introduced a confidence measure scheme in a bimodal camera setup for automatically selecting visible-light or thermal infrared in response to natural environmental changes. The purpose of the setup is to robustly detect people in dynamic outdoor scenarios under very different conditions.  
Visual-light and thermal infrared sensors possess advantages and disadvantages that are complementary. On the one hand, thermal infrared cameras are useful for detecting humans under adverse illumination conditions. On the other side, visible-light cameras yield good results when conducting human detection in well lit environments, but it is problematic in dark environments or in areas of the scene that present shadows or have low visibility in general. In order to take advantage of both spectra, some researchers have decided to perform image fusion by using visible and infrared images together. However, as the fusion of thermal infrared and visible images is difficult, we have opted for using a selection-based approach that switches from one sensor to another depending on the environmental conditions of the scene.  
The approach is based on confidence measures that are set for each spectrum using the mean illumination, the standard image deviation in thermal infrared, and the average grey level value in visible-light. In the visible-light spectrum, there are three different confidence intervals separated by two confidence threshold values. The confidence value in the visible spectrum takes the linguistic values \(HIGH\) and \(LOW\), corresponding to favorable and adverse illumination conditions, respectively. A \(MEDIUM\) confidence value is not used in the visible-light spectrum.  
In the thermal infrared spectrum, there are three possibilities for assigning the confidence intervals, as the confidence value in thermal infrared is directly related to the value in visible-light. When the confidence value of the visible-light spectrum is set to \(LOW\), it is clear that the visible-light sensor is almost unable to distinguish humans. This is why the infrared spectrum is always forced with a confidence value above \(LOW\) (that is, \(MEDIUM\) or \(HIGH\)). Indeed, under this circumstance, the infrared sensor always works better than the visible-light one. On the other hand, when the detection in visible-light offers good results (\(HIGH\) confidence value), the confidence value in the thermal infrared spectrum can take values \(LOW\), \(MEDIUM\) or \(HIGH\).  
In order to evaluate the proposal, two efficient segmentation algorithms, one dedicated to the visible-light spectrum and another one to the thermal infrared spectrum, were implemented and five different video sequences were recorded under very different environmental conditions. The results of the segmentation in both spectra permitted setting up the best-suited confidence interval thresholds. Moreover, the results obtained confirmed the accuracy of the approach.  
This work was partially supported by the Spanish Ministerio de Economi y Competitividad/FEDER under the TIN2013-47074-C2-1-R and TIN2010-20845-C03-01 grants.  
The three authors contributed to the different phases of the research and to the writing of this paper.  
The authors declare no conflict of interest.