Due to the advantages of high mobility and the ability to fly in the sky, drone has inspired more and more applications in recent years. On the other hand, deep learning-based human activity analysis is an important research topic in security surveillance; however, there are few research works on such analysis with aerial images so far. Because of perspective projection, people in aerial images look tilted, which would degrade the performance of human activity analysis. In order to cope with the issue of perspective projection for aerial images, we modify the CNN architecture of a state-of-the-art object detection method, YOLOv2 , and build an aerial image dataset with a drone for new model training. Finally, a post-processing method is proposed to classify the pose of a detected person as normal or abnormal, so that the task of human activity analysis with aerial images can be accomplished.