Semantic part RCNN for real-world pedestrian detection

Mengmeng Xu, Yancheng Bai, Sally Sisi Qu, Bernard Ghanem

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

5 Scopus citations

Abstract

Recent advances in pedestrian detection, a fundamental problem in computer vision, have been attained by transferring the learned features of convolutional neural networks (CNN) to pedestrians. However, existing methods often show a significant drop in performance when heavy occlusion and deformation happen because most methods rely on holistic modeling. Unlike most previous deep models that directly learn a holistic detector, we introduce the semantic part information for learning the pedestrian detector. Rather than defining semantic parts manually, we detect key points of each pedestrian proposal and then extract six semantic parts according to the predicted key points, e.g., head, upper-body, left/right arms and legs. Then, we crop and resize the semantic parts and pad them with the original proposal images. The padded images containing semantic part information are passed through CNN for further classification. Extensive experiments demonstrate the effectiveness of adding semantic part information, which achieves superior performance on the Caltech benchmark dataset.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PublisherIEEE Computer Society
Pages45-54
Number of pages10
ISBN (Electronic)9781728125060
StatePublished - Jun 2019
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Duration: Jun 16 2019Jun 20 2019

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2019-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Country/TerritoryUnited States
CityLong Beach
Period06/16/1906/20/19

Bibliographical note

Funding Information:
Acknowledgments This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research and by Natural Science Foundation of China, Grant No. 61603372.

Publisher Copyright:
© 2019 IEEE Computer Society. All rights reserved.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Semantic part RCNN for real-world pedestrian detection'. Together they form a unique fingerprint.

Cite this