## 4 Results
### Training and Testing Details  
We choose SlowFast as a comparison model because it is a very successful CNN approach and in particular because MAROON is inspired by SlowFast and extends upon its ideas. MViT is a very prominent transformer model that performs well in action recognition on person data. We choose the parameters for these models following the values proposed by the respective papers and through various model tests. We base the maximum length of the input sequences on the action classes, which describe temporally short actions such as head raising or a sudden rush. For the Rolandseck Daylight dataset, we choose an input length of 16 frames. Since this dataset has a high FPS of 30, fast actions are also well covered. For the Bavarian Forest Daylight and Bavarian Forest Nighttime datasets, we have to limit ourselves to eight frames. As shown in Section 3, the action class distributions in our datasets are long-tailed distributions. This means that the underrepresented classes have to be specifically considered to achieve good predictions due to the low number of samples during training. To achieve better action recognition results on underrepresented classes, we perform oversampling during the training process. In each epoch, we randomly sample from the smaller classes, so that each class represents at least 75% of the number of observations of the largest class. We determine this parameter through experiments. We restrict the resampling to 75% because our smallest classes only contain 5 samples and the largest class more than 100 samples. Even with data augmentation strategies, these samples are presented to the network very frequently. With this restriction, we try to prevent overfitting on the individual samples. During training, we use random horizontal flips as data augmentation. Moreover, we perform temporal and spatial jittering. In general, the action sequences are longer than the exact input length for the networks, in our case, 16 or 8 frames. For temporal jittering, this desired sequence of fixed length is randomly sampled from the whole action sequence. With temporal jittering, the model receives a different part of the entire action sequence each epoch, avoiding overfitting. As described before, we cut out each animal by their bounding box. For spatial jittering, the box is cut out a little bit larger than the desired input size for the network. Then, the correct size is cut out randomly from this larger patch. With this technique, the animal is not always at the center position and therefore there is more variety in the training data. For comparable testing, the input must always be the same. Accordingly, spatial jittering is not performed. However, in order to cover several points in time of an action sequence, the sequence is divided into equal sections as test sequences. This is called ensemble view testing. We use 10-view testing. For evaluating the action recognition quality of networks, we use the common metric of top-1 and top-5 accuracy. In general, top-\(k\) accuracy means that there is a correct prediction for a sample if the \(k\) best predictions of the network include the correct action class of the sample. There is no common metric for the task of action detection. However, the mean average precision (mAP) is sometimes used (for example, in [52; 53]). One problem with this metric is that it only takes into account the bounding box accuracy of the detector (and not instance masks, for example) and therefore creates a high dependency of the action class prediction on the detection. By evaluating the detector and the action recognition approach separately, both parts are evaluated more fairly, and at the same time it is easier to decide whether a part of the overall system should be replaced. We train all our models for 200 epochs. To ensure a fair comparison among the models, we do not use pretrained weights for initialization. For MAROON and also for SlowFast, we use the ResNet-50 as the backbone. For the Rolandseck Daylight dataset, the input sequence length is 16 frames.