Counting forest road users on digital still images by machine learning
Abstract
Mixed use of the forest road network is permitted by law in Hungary. Managers of frequently visited forests should know the traffic of their road network in order to minimize conflicts between the road users. In this article an experimental traffic counter is presented. The tool takes digital still images and is able to assess road user categories and numbers by image recognition. The counter records 1MP images on the signal of a pair of retro-reflexive optical sensors mounted on the roadside. The device had been in operation throughout a year. It was learned that the stable fixing of the sensors and the elevated position of the camera is crucial for the operation. The images were assessed by the adapted version of the YOLO9000, which is a neural net based object detection system. The YOLO9000 was parametrized by humanassessment of 10.000 images of the counter. 94-95% of pedestrians and cyclists while 85% of cars were correctly recognized by the adapted system. Our results showed that it is possible to successfully use free, open-source image recognition tools for visitor counting in a natural environment.
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