## 5 Discussion
Action recognition datasets usually contain only video sequences focused on the actor. Action detection datasets usually use only bounding boxes and no instance masks to localize the different actors, for example, the person action detection dataset AVA [58]. Since we use SWIFT as a reliable instance segmentation and tracking tool in our action detection system, an application to fallow deer, red deer and roe deer is not problematic. However, if other animal species or humans are to be examined, SWIFT must first be trained with appropriate instance masks. If no dataset with instance masks is available, this means that annotations with instance masks must be created for the desired new animal species. In principle, this is more time-consuming than an annotation with bounding boxes. However, as annotation tools with AI support are constantly being developed further [56; 59] and foundation models such as Segment Anything [60] are emerging at the same time, this may become an easier task in the future than it is today. In Section 4.2, we present the class-specific action recognition accuracies for our models and datasets. There are two reasons why both our MAROON approach and the other two action recognition networks achieve lower accuracies on some classes than on others. One simple reason is that some classes (like resting, standing up or a sudden rush) occur less frequently than others, as already discussed. Even if this problem is partially compensatedfor by the introduced oversampling, it may still happen that the training data are not diverse enough to generalize successfully. A targeted enlargement of the dataset with regard to these classes (given that there are sufficient recordings that show this behavior) is therefore a possibility to improve the prediction accuracy. The second reason why some action classes are difficult to predict is that they can be performed differently and also may be similar to other classes. Examples of this are the classes foraging moving and walking. Foraging moving differs from the action class walking only in the aspect that the animal searches for food. For example, it is characteristic that the animal's head is lowered in comparison to walking. The action classes foraging standing and vigilant standing can also be very similar, as the animal's head is not lowered, particularly when it is searching for food, for example, at a bush. Here, too, increasing the amount of samples can make it easier to distinguish between the classes. But, in general, it can be seen that the action recognition accuracies for these classes are higher than for the classes belonging to the first reason mentioned. When an action detection system is applied to a video where animals change their behavior (which is a normal case), there are transition areas between two behaviors. In these, the prediction changes from one behavior to the next. The findings of the analysis of behavioral sequences from ethology [61; 62; 63] could be helpful in the future to support the action prediction process. In addition, these studies can also be used for persons [64]. For example, there are behaviors that are more likely to follow other behaviors. This initially has no effect on the evaluation of the action recognition approaches, as only one sequence in which only one behavior occurs is considered here. However, this could be helpful when applying the system to unseen recordings. This additional information could make the prediction by action recognition even more accurate or verify its results.