  In this paper we propose a method based on deep learning that detects
multiple people from a single overhead depth image with high reliability. Our
neural network, called DPDnet, is based on two fully-convolutional
encoder-decoder neural blocks based on residual layers. The Main Block takes a
depth image as input and generates a pixel-wise confidence map, where each
detected person in the image is represented by a Gaussian-like distribution.
The refinement block combines the depth image and the output from the main
block, to refine the confidence map. Both blocks are simultaneously trained
end-to-end using depth images and head position labels. The experimental work
shows that DPDNet outperforms state-of-the-art methods, with accuracies greater
than 99% in three different publicly available datasets, without retraining not
fine-tuning. In addition, the computational complexity of our proposal is
independent of the number of people in the scene and runs in real time using
conventional GPUs.
