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Author (up) Mayer, M.; Staab, J.; Udas, E.; Taubenbock, H., pdf  url
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  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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