Automatic Aircraft Detection and Classification in Cluttered Environments
نویسندگان
چکیده
A vision-based system for detecting and classifying moving obstacles in a cluttered environment using a single camera mounted on a small unmanned aerial vehicle is presented. Feature correspondences between successive image frames are found using the SIFT algorithm, and a model of the background motion is generated by Random Sample Consensus (RANSAC). The transformed frames are then differenced to produce a clean image of any objects moving relative to the background. These object silhouettes are then classified as airplane vs. non-airplane in order to reject false positives and to improve obstacle state-estimation information.
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