Comparing established visitor monitoring approaches with triggered trail cameras images and machine learning based computer vision

Abstract

The management of protected areas and other recreational landscapes is subject to a variety of challenges. One aspect hereof, visitor monitoring, is crucial for many management and valuation tasks of ecosystem services. Its core data are visitor numbers which are costly to estimate in absence of entry fees for protected areas or recreational landscapes. 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. To overcome this limitation, we deployed a convolutional neural network and compared its hourly counts against existing visitor counting methods such as manual in-situ counting, a pressure sensor, and manual camera image evaluations. Our study is the first one to implement, and explicitly assess the performance of a computer vision approach for visitor-monitoring. The results showed that the convolutional neural network derived comparable visitor numbers to the other visitor counting approaches regarding visitation patterns and numbers of visits. Further, our approach also allowed for counting dogs and recreational equipment such as backpacks and bicycles in automatic manner. We thus conclude that it is a fast and reliable method that could be used in protected areas as well as in a much wider array of visitor counting settings in other recreational landscapes.