Object detection and tracking in drone imagery is still an open research field, especially for livestock monitoring and when detection is carried out on the drone itself. In this paper, we present the first annotated aerial video dataset of sheep, which we will make publicly available to the research community to foster further research in this field. Our AUTH-Sheep dataset consists of 4 videos with frame-accurate annotations of oriented bounding boxes and consistent track IDs per object and video. Furthermore, we developed a full detection and tracking pipeline as a baseline implementation to give other researchers a reference approach to compare their algorithms against. For this, we compared horizontal and oriented bounding box detection for the task at hand. Therefor, the YOLOv8 nano detector is utilized, which was pre-trained on a different dataset. To be able to train this detector of oriented bounding boxes, we semi-automatically created new oriented annotations for an existing dataset of sheep images.