## 5 Discussion
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Dataset** & **Type of Lateral Connection** & **Top-1** & **Top-5** \\ \hline \multirow{3}{*}{Rolandseck} & fast to slow & **69.16** & **96.31** \\  & no connection & 65.50 & 95.70 \\  & medium (before merging) to slow & 66.74 & 94.32 \\  & medium (after merging) to slow & 68.26 & 96.00 \\ \hline \multirow{3}{*}{Bavarian Forest Daylight} & fast to slow & 46.39 & 97.24 \\  & no connection & 45.71 & **97.94** \\  & medium (before merging) to slow & **51.89** & 96.21 \\  & medium (after merging) to slow & 46.38 & 96.92 \\ \hline \multirow{3}{*}{Bavarian Forest Nighttime} & fast to slow & **43.05** & 96.33 \\  & no connection & 42.35 & **96.93** \\ \cline{1-1}  & medium (before merging) to slow & 39.18 & 95.10 \\ \cline{1-1}  & medium (after merging) to slow & 41.70 & 95.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of the lateral connections to the new MAROON slow pathway. The best value for each dataset is marked in bold. humans. Our approach would need to be adapted so that multiple animals are masked as input and entered into the action recognition network to also describe interactions between animals. The application of our action detection system to other animal species, especially to other similarly sized animals, is easy to implement in principle. Data material for the desired animal species and the corresponding actions must first be available and annotated in the same way. SWIFT and MAROON must then be trained for the new animal species and action classes. As the detection by the Mask R-CNN in SWIFT is very reliable and can be used for many objects, a transfer is easily possible in most cases. If small animals such as birds are to be examined, it must be taken into account that the animals may only be represented by a very small region within the video (e.g., a size of less than 50 \(\times\) 50 pixels). This can be problematic for the detection and even more problematic for action recognition due to the missing visual details. Care should therefore be taken to ensure that a correct camera setting is used when recording data. In general, the resolution of the video recordings is important for recognizing the animals. It is easier for instance segmentation with SWIFT to segment more object details if the animal is closer to the camera and therefore has more pixels. As a result, a better image resolution also means easier recognition of the action classes in the action recognition part. However, the action recognition ability also depends on the action classes under consideration, i.e., how many details of the animal are necessary to recognize the action and whether there are similar actions with which it could be confused. If our approach is to be transferred to other animal species, the video resolution must also be taken into account. If our system is to be used for smaller animals or for birds, for example, they would probably be too small in our camera setting to distinguish between several actions. The recordings from the Rolandseck dataset already have a very high resolution. It is difficult to increase the video quality further because of storage restrictions on the camera trap devices. Therefore, the recording settings should be adapted to the desired animal classes and action classes. The recording setting (including the proximity of the camera to the animals or the location of the recording) should be adjusted accordingly. Extending the idea of SlowFast [22] and using three pathways for MAROON improves the action recognition accuracy. A drawback from the expansion of the model is that it now has more parameters than SlowFast and therefore takes more time to train. Thus, if very large datasets are used, either longer training times must be scheduled or more hardware must be used for the training process. The introduction of masked input sequences has improved our action prediction accuracy by an average of 10 percentage points. However, this also means that an instance mask must be available for the different animals.