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Author (up) Mayer, M.; Staab, J.; Udas, E.; Taubenbock, H.,
Title Triggered trail camera images and machine learning based computer vision as alternative to established visitor monitoring approaches? Type
Year 2021 Publication The 10th MMV Conference: Managing outdoor recreation experiences in the Anthropocene – Resources, markets, innovations Abbreviated Journal
Volume MINA fagrapport Issue Pages 296-297
Keywords MMV10
Abstract Visitor monitoring is crucial for many management and valuation tasks in protected areas and other recreational landscapes. Its core data are visitor numbers which are costly to estimate in absence of entry fees. Camera-based approaches have the potential to be both, accurate and deliver comprehensive data about visitor numbers, types and activities. So far, camera-based visitor monitoring is, however, costly due to time consuming manual image evaluation (Miller et al. 2017). To overcome this limitation, we deployed a convolutional neural network (CNN) and compared its hourly counts against existing visitor counting methods such as manual in-situ counting, a pressure sensor, and manual camera image evaluations.
Call Number Serial 4332
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Author (up) Staab, J.; Taubenbock, H.; Hob, H.
Title Monitoring Visitor Numbers with computer vision Type
Year 2018 Publication Monitoring and Management of Visitor Flows in Recreational and Protected Areas – ABSTRACT BOOK Abbreviated Journal
Volume MMV 9 - Proceedings Issue Pages 127-129
Keywords MMV9
Abstract Utilizing cameras to count visitors has proven to be accurate, traceable and rich in features (Arnberger et al., 2005). However, extracting data from the imagery manually consumes large resources, limiting the utilization of camera observations to short-term monitoring projects. In this work, we apply and test computer vision to characterize visitors at the Biosphere Reserve Schorfheide-Chorin in Germany in an automatic manner.
Call Number Serial 4080
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