Learning-based human detection applied to RGB-D images

Patrisia Sherryl Santoso, Hsueh-Ming Hang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Accurate human detection is still a challenging topic due to complicated environments in the real world. In addition, the RGB-D cameras are becoming popular at reasonable price, such as Microsoft Kinect sensor, which provides both RGB and depth data. The depth information often helpful for detection. We adopt the R-CNN method in this paper, which combines the Selective Search technique to generate region proposals and the CNNs (Convolutional Neural Networks) to learn features. A depth map encoding technique (HHA) is adopted to match the CNNs format for learning features. The HHA and RGB images are our inputs. We propose several algorithms to combine their information in constructing various human detectors. Our information fusion structures include CNN, SVM together with PCA for features reduction. More accurate human detection results are shown with the aid of depth information.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages3365-3369
Number of pages5
ISBN (Electronic)9781509021758
DOIs
StatePublished - 20 Feb 2018
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 17 Sep 201720 Sep 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Conference

Conference24th IEEE International Conference on Image Processing, ICIP 2017
CountryChina
CityBeijing
Period17/09/1720/09/17

Keywords

  • CNNs
  • Depth map
  • HHA depth encoding
  • Human Detection
  • RGB-D fusion

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