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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. |
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