Guven, AliYetik, İmam Şamil2024-09-222024-09-222024979835038897897983503889612165-0608https://doi.org/10.1109/SIU61531.2024.10601068Recently, providing real-time navigation of unmanned aerial vehicles independent of global positioning systems has become of great importance. The state-of-the-art methods based on deep learning, which give good results in certain datasets, and the existing methods can not provide real-time and good solutions on images with dynamic and fast moving. Moreover, the methods, were developed so far, were focused on object-based tracking algorithms. In this paper, the tracking of the points belonging to the target pattern, found by image matching, was performed with the machine learning model we developed for 10 sequential video images. The features extracted for the machine learning model are: (i) the change between the points of the previous image and the image before that, (ii) the points of interest in the previous image, (iii) the changes found with the homography matrix between sequential images. It was experimentally shown that, point tracking can be achieved with the least error, on avarage about 23 pixels for a 2 mega-pixel resolution image, among the algorithms in the literature that can process more than 30 images per second in a CPU environment of 2 GHz or above.trinfo:eu-repo/semantics/closedAccessImage MatchingImage HomographyImage-Object TrackingImage-Point TrackingApattern DetectionMachine LearningImage ProcessingFeature ExtractionConference Object2-s2.0-8520090081910.1109/SIU61531.2024.10601068