## 5 Discussion  
Our evaluation results show that MAROON outperforms other state-of-the-art approaches for the different datasets. Our three datasets cover various situations and scenes. In the Rolandsed Daylight dataset, groups of animals are often present, where a distinction of individual animals through instance masks is very important to obtain reliable action predictions. The Bavarian Forest Daylight dataset includes videos with only a few animals and additionally has a lower resolution and FPS rate. The Bavarian Forest Nighttime dataset shows that MAROON is also able to predict actions on videos without color information and with a very low FPS rate. In general, an easy solution to improve the accuracy of all models for the different datasets would be to increase the number of videos and therefore increase the data material for training and testing. However, the annotation process of the videos is very time-consuming and rare action classes are often underrepresented in the data. It is a common phenomenon in wildlife ecology [18] and in computer vision as a whole [57] that action classes occur very unevenly and are represented by long-tailed distributions. If the (very) rare action classes could be supplemented by further examples, this would improve the accuracy of the models. However, this is limited not only by the annotation but also by the rareness of the camera trap recordings of these actions. Often, these actions are of special interest to researchers. Therefore, we apply oversampling, one of the most widely used techniques to train long-tailed distributions more efficiently. Due to the very small number of some actions, undersampling is not useful in order not to restrict the data diversity for the more frequent classes too much. In order to generate a larger variety of data during training, we use the data augmentation techniques already explained in Section 4.1, such as horizontal flips, temporal jittering and spatial jittering. If sufficient time and resources are available for a study, it is also possible to make additional video recordings in zoos or wild animal enclosures in order to generate data material for rare behavior. Another way to supplement data from rare behaviors is to generate artificial data. Even if the results of today's artificially generated images are already very high, it is still problematic to transfer a network trained on artificially generated images to real images when considering such complex scenes as wildlife videos with changing weather conditions, different exposures and day and night recordings. However, with further optimization of the generation of artificially generated data material, this is a promising possibility to supplement the data material in the future. Our action recognition network is limited to the detection of actions of single animals, i.e., no interactions between animals can be detected. In our recorded data, this case was extremely rare and therefore neglected. Furthermore, most of the action recognition approaches concentrate on the identification of actions of one actor. But there are certainly other species and datasets where actions between different animal individuals are also of interest. The action detection approaches as shown in [51; 52] determine actions between