Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images   Human pose estimation (HPE) is a key building block for developing AI-based
context-aware systems inside the operating room (OR). The 24/7 use of images
coming from cameras mounted on the OR ceiling can however raise concerns for
privacy, even in the case of depth images captured by RGB-D sensors. Being able
to solely use low-resolution privacy-preserving images would address these
concerns and help scale up the computer-assisted approaches that rely on such
data to a larger number of ORs. In this paper, we introduce the problem of HPE
on low-resolution depth images and propose an end-to-end solution that
integrates a multi-scale super-resolution network with a 2D human pose
estimation network. By exploiting intermediate feature-maps generated at
different super-resolution, our approach achieves body pose results on
low-resolution images (of size 64x48) that are on par with those of an approach
trained and tested on full resolution images (of size 640x480).

13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat