Moving Object Detection is one of the integral tasks for aerial reconnaissance and surveillance applications. Despite the problem's rising potential due to increasing availability of unmanned aerial vehicles, moving object detection suffers from a lack of widely-accepted, correctly labelled dataset that would facilitate a robust evaluation of the techniques published by the community. Towards this end, we compile a new dataset by manually annotating several sequences from VIVID and UAV123 datasets for moving object detection. We also propose a feature-based, efficient pipeline that is optimized for near real-time performance on GPU-based embedded SoMs (system on module). We evaluate our pipeline on this extended dataset for low altitude moving object detection. Ground-truth annotations are made publicly available to the community to foster further research in moving object detection field.