Drones became popular video capturing tools. Drone videos in the wild are first captured and then edited by humans to contain
aesthetically pleasing camera motions and scenes. Therefore, edited drone videos have extremely useful information for cinematography
and for applications such as camera path planning to capture aesthetically pleasing shots. To design intelligent
camera path planners, learning drone camera motions from these edited videos is essential. However, first, this requires to
filter drone clips and extract their camera motions out of these edited videos that commonly contain both drone and non-drone
content. Moreover, existing video search engines return the whole edited video as a semantic search result and cannot return
only drone clips inside an edited video. To address this problem, we proposed the first approach that can automatically retrieve
drone clips from an unlabeled video collection using high-level search queries, such as “drone clips captured outdoor in
daytime from rural places". The retrieved clips also contain camera motions, camera view, and 3D reconstruction of a scene
that can help develop intelligent camera path planners. To train our approach, we needed numerous examples of edited drone
videos. To this end, we introduced the first large-scale dataset composed of edited drone videos. This dataset is also used for
training and validating our drone video filtering algorithm. Both quantitative and qualitative evaluations have confirmed the
validity of our method.