Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RGB-D image-based Object Detection: from Traditional Methods to Deep
  Learning Techniques   Object detection from RGB images is a long-standing problem in image
processing and computer vision. It has applications in various domains
including robotics, surveillance, human-computer interaction, and medical
diagnosis. With the availability of low cost 3D scanners, a large number of
RGB-D object detection approaches have been proposed in the past years. This
chapter provides a comprehensive survey of the recent developments in this
field. We structure the chapter into two parts; the focus of the first part is
on techniques that are based on hand-crafted features combined with machine
learning algorithms. The focus of the second part is on the more recent work,
which is based on deep learning. Deep learning techniques, coupled with the
availability of large training datasets, have now revolutionized the field of
computer vision, including RGB-D object detection, achieving an unprecedented
level of performance. We survey the key contributions, summarize the most
commonly used pipelines, discuss their benefits and limitations, and highlight
some important directions for future research.

20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex