dc.contributor.author | Ganglberger, Florian | en_US |
dc.contributor.author | Bühler, Katja | en_US |
dc.contributor.editor | Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia | en_US |
dc.date.accessioned | 2019-09-03T13:49:04Z | |
dc.date.available | 2019-09-03T13:49:04Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-3-03868-081-9 | |
dc.identifier.issn | 2070-5786 | |
dc.identifier.uri | https://doi.org/10.2312/vcbm.20191231 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/vcbm20191231 | |
dc.description.abstract | In recent years, radar technology has increasingly been used for the monitoring of bird migration. Marine radars are often utilized for this purpose because of their wide accessibility, range, and resolution. They allow the tracking of birds even at night-when most bird migration takes place-over extended periods of time. This creates a wealth of radar images, for which manual annotation of bird tracks is not feasible. We propose a tool for automatic bird tracking and visualization from marine radar imagery. For this purpose, we developed a bird tracking algorithm for vertically recorded radar images that is able to extract quantitative parameters including flight direction, height, and duration. The results can be qualitatively verified by a visualization design that enables domain experts the time-dependent visualization of bird tracks. Furthermore, it allows a preprocessing of radar images taken by screen capturing for device independence. Our tool was used in an ornithological monitoring study to analyze over 200.000 vertically recorded radar images taken in multiple observation periods and locations. | en_US |
dc.publisher | The Eurographics Association | en_US |
dc.title | Feasibility Study For Automatic Bird Tracking and Visualization from Time-Dependent Marine Radar Imagery | en_US |
dc.description.seriesinformation | Eurographics Workshop on Visual Computing for Biology and Medicine | |
dc.description.sectionheaders | Visual Analytics in Medicine and Biology | |
dc.identifier.doi | 10.2312/vcbm.20191231 | |
dc.identifier.pages | 51-55 | |