###### Abstract
They also found biases of some cathemeral species frequently active during daylight. This is because, as expected, the authors did not train the ML algorithm to detect and classify diurnal species in grayscale images, and cathemeral species that are commonly active during daylight naturally lack grayscale images for model training. The biases caused such species to be commonly classified as cathemeral species, including _Crocuta crocuta_. Nonetheless, this study still resulted in 88.9% accuracy, using deep regions with convolutional neural networks (R-CNNs) to detect and correctly classify species in CT images from the Snapshot Serengeti dataset. No known study has focused on the influences of human-related factors and their effect on human DP and CC in CT images. That said, [PERSON] et al. [2] used thermal infrared (TIR) and red, green, and blue (RGB) drone imagery. They found human occlusion due to canopy density, increasing distance from the centreline--the centre of an experiment created by the investigator, and analyst performance were the main influences of human DP within TIR drone imagery. The authors also assessed clothing colour contrast relative to the background and its influence on human detection, finding that red and blue were the most influential colours for detecting humans using RGB drone imagery. Although many studies focus on varying factors and their influence on wildlife detection and classification in CT images, no study has focused explicitly on the potential influence of external factors on the DP and CC of wildlife and humans in CT images. Therefore, in this study, we assessed the potential influence of the following factors: species (characteristics), analyst performance, vegetation type, degree of occlusion, distance from the CT, time-of-day, human/wildlife (target) height (metres), colour contrasts of clothing, and orientation towards the CT on the DP and CC of targets within CT images (see Figure 1 for a visual overview of the tested factors within CT images). Additionally, we investigated multi-factor influences on ML performance compared with analyst performance for true-positive detection and classifications. We collected the data during the standard operating of the cameras within western Tanzania. Afterwards, we collated the training and testing data to assess the influence of the tested factors on target DP and CC within CT images, using ML and analyst methods. We then compared the performance of these methods for true-positive detections and classifications. We initially proposed an alternative hypothesis for each factor tested. This includes:  
* A human or animal's increase in distance and occlusion simultaneously from the CT would negatively influence at least one experiment, and denser vegetation cover would further contribute to this influence for one or more models. * A darker (green) colour contrast in comparison to the background would negatively influence human DP. * Decreases in the size of humans or wildlife would decrease their probability of detection, respectively, and darker times of day (dusk) would negatively affect human or wildlife DP and CC. * Occluded or partially occluded characteristic traits for similar species would decrease wildlife CC. * DP and CC would increase with increased human or animal orientation towards the CT.
* There would be a significant positive difference in analyst performance on target DP and CC. * Lastly, the ML method would present higher or equivalent true-positive detections and classifications than the analyst method for one or more experimental models. Figure 1: Examples illustrating different CT image scenarios, incorporating the factors measured in this study.