This paper presents a method to classify human activities as normal or suspicious using YOLOv3 to automatically process video footages taken from a high altitude moving aerial camera, such as the one attached to a drone. We consider four human activities namely, jogging, walking, fighting, and chasing. Objects generally appear much smaller, with less visible features, when viewed from high altitudes. The reduced visible features make automatic human activity detection from ground surveillance cameras not applicable to the high altitude case. Through transfer learning, we modified existing pre-trained YOLOv3 convolutional neural networks (CNN's) and retrained with our own high aerial human action dataset. By so doing, we were able to customize YOLOv3 to detect, localize, and recognize aerial human activities in real-time as normal or suspicious. The proposed approach achieves a promising average precision accuracy of 82.30%, and average F1 score of 88.10% on classifying high aerial human activities. We demonstrated that YOLOv3 is a powerful approach and relatively fast for the recognition and localization of human subjects as seen from above.