Print Email Facebook Twitter Adaptive real-time clustering method for dynamic visual tracking of very flexible wings Title Adaptive real-time clustering method for dynamic visual tracking of very flexible wings Author Mkhoyan, T. (TU Delft Aerospace Structures & Computational Mechanics; TU Delft Arts & Crafts) de Visser, C.C. (TU Delft Control & Simulation) De Breuker, R. (TU Delft Aerospace Structures & Computational Mechanics) Date 2021-01-01 Abstract Advancements in aircraft controller design, paired with increasingly flexible aircraft concepts, create the need for the development of novel (smart) adaptive sensing methods suitable for aeroelastic state estimation. A potentially universal and noninvasive approach is visual tracking. However, many tracking methods require manual selection of initial marker locations at the start of a tracking sequence. This study aims to address the gap by investigating a robust machine learning approach for unsupervised automatic labeling of visual markers. The method uses fast DBSCAN and adaptive image segmentation pipeline with hue-saturation-value color filter to extract and label the marker centers under the presence of marker failure. In a comparative study, the DBSCAN clustering performance is assessed against an alternative clustering method, the disjoint-set data structure. The segmentation-clustering pipeline with DBSCAN is capable of running real-time at 250 FPS on a single camera image sequence with a resolution of 1088×600 pixels. To increase robustness against noise, a novel formulation (the inverse DBSCAN, DBSCAN−1 ) is introduced. This approach is validated on an experimental dataset collected from camera observations of a flexible wing undergoing gust excitations in a wind tunnel, demonstrating an excellent match with the ground truth obtained with a laser vibrometer measurement system. To reference this document use: http://resolver.tudelft.nl/uuid:e6f3928b-3a41-40f1-b640-f8c774e6d50d DOI https://doi.org/10.2514/1.I010860 ISSN 2327-3097 Source Journal of Aerospace Information Systems (online), 18 (2), 58-79 Part of collection Institutional Repository Document type journal article Rights © 2021 T. Mkhoyan, C.C. de Visser, R. De Breuker Files PDF 1.i010860.pdf 13.76 MB Close viewer /islandora/object/uuid:e6f3928b-3a41-40f1-b640-f8c774e6d50d/datastream/OBJ/view