Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DPDnet: A Robust People Detector using Deep Learning with an Overhead
  Depth Camera   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.

10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th