Vehicle detection based on semantic component analysis

Guosheng Cui, Qi Wang, Yuan Yuan

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

1 Scopus citations

Abstract

Vehicle detection is a hot topic in trafic monitoring applications. Though many researchers has done a lot work towards this direction, the detection in occluded conditions is rarely explored and it still remains a challenge. In this work, we focus on the occlusion problem in vehicle detection and propose a novel method based on semantic component analysis and scale consideration. Two contributions are claimed in this procedure: 1) Tackling vehicle detection by semantic component detection and synthesis. 2) Addressing the scale variation of vehicles by simple yet effective standard component definition. The experimental results on two typical surveillance videos show that the proposed method can effectively detect the vehicles in the crowded trafic conditions with occlusion.

Original languageEnglish
Title of host publicationICIMCS 2014 - Proceedings of the 6th International Conference on Internet Multimedia Computing and Service
PublisherAssociation for Computing Machinery
Pages172-176
Number of pages5
ISBN (Print)9781450328104
DOIs
StatePublished - 2014
Event6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014 - Xiamen, China
Duration: 10 Jul 201412 Jul 2014

Publication series

NameACM International Conference Proceeding Series

Conference

Conference6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014
Country/TerritoryChina
CityXiamen
Period10/07/1412/07/14

Keywords

  • Deformable part based model
  • Occlusion
  • Semantic component
  • Trafic monitoring
  • Vehicle detection

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