## 1 Introduction  
Understanding animal behavior is a fundamental component of conservation biology [1]. Consequently, action detection for wild animals has become an essential task for ecologists to assist conservation efforts [2]. However, quantifying the behavior of wild animals presents significant challenges and is often neglected as a result of the tremendous work-load that is needed to analyze all collected data.  
Camera traps have become an ubiquitous tool in ecology and conservation that offer a reliable, minimally invasive and visual means of surveying wildlife [2, 3, 4]. Over the last few decades, camera traps have been adopted for various ecological tasks, including abundance estimation [5; 6; 7], the quantification of species diversity [8], the detection of rare species [9], the investigation of animal activity patterns [10], and the analysis of species replacement processes [11]. Automatic analysis using artificial intelligence is absolutely necessary to deal with the vast amount of collected camera trap data [12; 13]. Currently, many AI models are created that enable the management and processing of camera trap images and videos, facilitate the categorization of camera trap images, or are able to classify species within these images [14]. Although a few AI models exist that allow the classification of species in camera trap videos, a tool for the automatic quantification of animal behavior is still lacking.  
Action recognition or action classification describes the task of assigning an action label to a (short) video sequence [15; 16]. In that video sequence, only one actor is performing one action. In contrast, action detection is the combined task of detecting an object in a video and then performing action recognition on the found object. Therefore, with action detection it is possible to describe different actions from different actors at the same time in a video. Sometimes, however, the term action detection is used not only in a spatial context but also in a temporal context, meaning that it is not the person or animal that is spatially detected in a video but the start and end point of an action within a video that is determined. In this case, (temporal) action detection again refers to a single individual in a video and not to multiple actors.  
Action recognition and action detection for wildlife monitoring are still relatively unexplored research areas. The following approaches represent all the important work in the field of action recognition and action detection in wildlife monitoring. General action recognition and action detection approaches are presented in Section 2. In our previous work [17], we considered action recognition for different European animals with just three simple action categories (walking, standing and eating). The work of [18] creates a neural network for the action recognition of great apes based on a self-created dataset. The Animal Kingdom Dataset is introduced in [19] and comprises action clips of a huge variety of animal species. The authors also propose an action recognition network, CARe, that recognizes the actions of unseen animal species. In the paper [20], the authors introduce a metric learning system with a triple-stream embedding network using RGB and optical flow features for the action recognition of great apes.  
This paper presents the first action detection approach for camera trap videos. We combine our successful instance segmentation system SWIFT (segmentation with filtering of tracklets; see Section 3 for more detail) [21] with our newly developed action recognition network MAROON (mask-guided action recognition). The two most important innovations in our action recognition network are (1) the use of masked input frames and (2) the triple-stream approach for the spatio-temporal feature extraction. We evaluate our action recognition network on three different datasets and compare it to the state-of-the-art networks SlowFast [22] and MViT [23].