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This paper presents a novel people detection approach for mobile robot applications based on a combination of classical computer vision techniques and a state-of-the-art neural network. Our approach involves an RGB-D camera as an environmental sensor. The depth data is used to extract silhouettes around people. The RGB images are subsequently augmented with this border information before passing it to the neural network. Under challenging lighting conditions, our system was able to outperform the neural network trained on regular RGB data alone by a factor of two.