Summary: Compared to stationary surveillance cameras, moving object detection on the camera carried by airborne vehicle is more difficult because of the relatively dynamic background. In this study, we present a hybrid method for moving object detection in aerial videos. We compensated the ego motion of airborne vehicle by feature-point based image alignment on consecutive frames, and then applied an accumulative frame differencing method to detect the pixels with motion. Meanwhile, the current frame was divided into homogeneous regions by image segmentation, and some of them were selected as candidates of moving objects by prior rules. The motion pixels and the moving object candidates were then fused by a morphing-based approach, obtaining position and shape of moving objects. Moreover, a Kalman-filter tracker was adopted to not only give a consistent label on each detected moving object and but also reject false alarms. The proposed method was evaluated on the videos captured on an airborne vehicle at different altitude. Experimental results revealed that the proposed hybrid method has better performance than both frame-difference and optical-flow based approaches.