Pedestrian detection using labeled depth data

Kwang Hee Won, Sisay Gurmu, Soon Ki Jung

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

5 Scopus citations

Abstract

This paper presents pedestrian detection algorithm on labeled depth data which is obtained from road scenes. Our approach computes feature responses for head and legs of human body using depth and label data. And then, it detects pedestrians by removing edges and partitioning a bipartite graph of head and leg response blobs using prior knowledge about human body. In the experiments, the proposed algorithm produces better result compared to the method which uses histogram of gradient feature and the ground plane for road scenes.

Original languageEnglish
Title of host publicationFCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision
Pages117-120
Number of pages4
DOIs
StatePublished - 2013
Event19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2013 - Incheon, Korea, Republic of
Duration: 30 Jan 20131 Feb 2013

Publication series

NameFCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision

Conference

Conference19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2013
Country/TerritoryKorea, Republic of
CityIncheon
Period30/01/131/02/13

Keywords

  • depth data
  • pedestrian detection
  • road scene

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