## 3 Results
### Analysing External Factor Influences on Human DP and CC  
Human detection within daylight CT images was highly significant (\(p<0.001\)) between 10-30 m with a negative influence on DP and CC, increasing as distance increased (see Figure 3a,b). Such positive influences of shorter distances on DP and CC decreased for dusk experiments using both ML and analyst methods and ML-CC (see Figure 3d,e). This highlights the difficulty of detecting and classifying humans in CT images under such conditions.  
Occlusion of 34-67% showed a highly significant (\(p<0.001\)) positive influence on human DP in dusk CT images using analysts (see Figure 3c), potentially due to open vegetation, increasing the DP of humans in CT images even at medium occlusion levels in dusk lighting conditions. However, increases in distance and occlusion significantly decreased human DPs in daylight CT images using ML methods and dusk CT images using analyst methods (see Figure 3a,d). This was increasingly true when combined with dense vegetation (riverine forest), causing a steep decline of human DP in daylight CT images at shorter distances (15 m) for both ML and analyst methods (see Figure 3a,d). Moreover, the model shows that detecting and classifying humans at dusk in open vegetation (miombo woodland) from the same distance caused more positive effects than detecting them in daylight within denser vegetation (riverine forest) (see Figure 3b,e). That said, vegetation type (\(p=0.981\)) and occlusion 34-67% and 68-100% (\(p=0.933\), 0.673), colour and distance 5-30 m (\(p=0.034\), 0.038, 0.027, 0.094, 0.164, and 0.387) presented non-significant effects on the ML-daylight-CC model (see Figure 3e). Moreover, 5 m distance presents non-significant effects on the ML (\(p=0.308\)) and analyst-daylight-DP (\(p=0.096\)) models (see Figure 3a,d). Occlusion of 34-67% and 68-100% also had a non-significant influence on the analyst-daylight-DP (\(p=0.155\), 0.095) and ML-dusk-CC models (\(p=0.704\), 0.995). Targets at a 10-m distance had a non-significant (\(p=0.923\)) effect on the ML-dusk-CC model (see Figure 3a,f).  
Furthermore, the ample amount of non-significant influential factors on human DP is likely due to the influence of naturally occurring outliers and the high rate of detections in daylight CT images. Additionally, the overall abundance of CCs for humans in both daylight and dusk CT images likely influenced the degree of non-significant influential factors for the CC models.  
On the other hand, darker contrast colours (green) significantly (\(p<0.001\)) negatively affected human DP in daylight CT images, using analyst methods (see Figure 3a), and presented insignificant effects on human CC in dusk CT images using ML methods (see Figure 3c). This result leads to the assumption that clothing colour contrast against the background mostly influenced the analyst's ability to detect humans within daylight CT images.  
However, human orientation towards the CT presented a significant (\(p<0.001\)) but weak positive influence on the DP of humans within daylight CT images, using analyst methods (see Figure 3a). This indicates that increased surface area relative to the CT was a significant predictor of human DP in daylight CT images.  
The models displayed significant variations between experimental locations, with the ML-dusk-CC model (SD 2.683) displaying the highest variation. This is followed by the analyst-dusk-DP model (SD 1.038), ML-d daylight-DP model (SD 0.806), analyst-daylight-DP model (SD 0.799), ML-dusk-DP model (SD 0.714), and ML-daylight-CC model (SD 0.326), respectively.