Supporting image-based wildlife classification

This work introduces a computer vision system designed for supporting users in imagebased wildlife classification. Leveraging deep learning techniques, the system employs oneand twostage neural network architecture to detect and classify different wildlife species from input camera trap images with accuracies up to 95%. Additional, a custom tailored data set is presented. The system demonstrates its efficacy in realworld scenarios, providing a valuable tool for wildlife monitoring and conservation efforts.