## 1 Introduction  
Action recognition involves automatically identifying and categorizing human actions in video sequences, which is highly beneficial and needed for surveillance applications [1, 2, 3]. Action recognition is, indeed, a challenging task due to the presence of various challenges, particularly background clutter occlusions and camera viewpoint [4, 5, 6]. Conventional action recognition methods involved hand-crafted feature extraction [7, 8], based on manually representing actions, such as motion, shape, or appearance descriptors. The limitations of this approach lie in the fact that hand-crafted features may not be able to effectively capture complex temporal relationships or variations in different action scenarios. Indeed, designing effective features could be challenging; plus, they may not generalize well to different datasets or action classes. The 3D CNNs extend the concept of traditional 2D CNNs [9, 10] to incorporate temporal information by processing video frames as 3D volumes. They, however, require a large amount of training data and computational resources. Additionally, they may struggle with long-term temporal dependencies or capturing fine-grained motion details. Moreover, the training of 3D CNNs from scratch can be challenging due to the limited availability of annotated video datasets. Recurrent neural networks (RNNs), gated recurrent unit (GRU), or LSTM [11, 12] model temporal dependencies by maintaining internal memory states. However, RNNs may struggle with modeling long-term dependencies or capturing complex spatial dynamics. They could be sensitive to the order and timing of actions within sequences. RNNs are computationally intensive, especially for longer sequences. Two-stream networks [13; 14] consist of the spatial stream (CNN for appearance) as well as the temporal stream (CNN or RNN for motion). They require synchronized and aligned RGB and optical flow inputs, which could be challenging to obtain in practice. Combining the information from two streams can introduce additional complexity and potential performance degradation. Graph convolutional networks (GCNs) [15; 16] represent actions as graphs and exploit graph convolution operations to capture spatial and temporal relationships between body joints or keypoints. However, GCNs rely heavily on accurate and reliable detection and the tracking of skeletal keypoints and also have limitations when dealing with occlusions or missing keypoints in complex action scenarios. Designing appropriate graph structures and defining graph convolution operations are, inevitably, challenging. The recent introduction of vision transformers has proved to be more efficient in accuracy. There are approaches that utilize transformers for action recognition [13; 17; 18] in complex scenarios; however, they are generally computationally more resource-consuming. Aerial videos provide a comprehensive view [5] of the scene, enabling surveillance operators to monitor larger areas and detect events that may otherwise be overlooked. Action recognition from aerial scenarios, however, requires reliable detection of the target in complex backgrounds, with varying camera angle altitudes for an accurate classification of the action [19; 20; 21; 22]. [PERSON] et al. [23] proposed a method that relied on extracting 2D skeletal data using OpenPose that are then fed into LSTM for training and testing. Their framework was, however, validated in an indoor multi-view scenario and may not be directly deployable for aerial videos. Another limitation in human action recognition is that the trained models generally misclassify when provided with unannotated data from new users [24], even after being trained on a large amount of data. This challenge arises as it is impractical to collect data for every new user. [PERSON] et al. [25] aimed to address this problem by presenting a semi-supervised learning action recognition method for training on labeled as well as unlabeled data but not primarily for the aerial camera settings that are under consideration in this paper.