RGB-D Image-Based Object Detection: From Traditional Methods to Deep Learning Techniques

Isaac Ronald Ward, Hamid Laga, Mohammed Bennamoun

Research output: Chapter in Book/Conference paperChapterpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationRGB-D Image Analysis and Processing
EditorsPaul L. Rosin, Yu-Kun Lai, Ling Shao, Yonghuai Liu
Place of PublicationNetherlands
Number of pages33
ISBN (Electronic)9783030286033
ISBN (Print)9783030286026
Publication statusE-pub ahead of print - 27 Oct 2019

Publication series

NameAdvances in Computer Vision and Pattern Recognition
ISSN (Print)2191-6586
ISSN (Electronic)2191-6594


Dive into the research topics of 'RGB-D Image-Based Object Detection: From Traditional Methods to Deep Learning Techniques'. Together they form a unique fingerprint.

Cite this