A Multi-View Pedestrian Tracking Framework Based on Graph Matching

Fanyi Duanmu, Xin Feng, Xiaoqing Zhu, Wai Tian Tan, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the applications of video monitoring over large public or private spaces, multiple cameras are required to cover the entire space and resolve the problems of occlusion, object intersection and so on. In this work, a novel multi-view pedestrian tracking framework is proposed to simultaneously detect and associate human objects across views using graph matching techniques to fully exploit the object features and the spatial/temporal relationships among the objects. Experimental results are provided to demonstrate the accuracy of our proposed framework.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages315-320
Number of pages6
ISBN (Electronic)9781538618578
DOIs
StatePublished - Jun 26 2018
Event1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018 - Miami, United States
Duration: Apr 10 2018Apr 12 2018

Publication series

NameProceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018

Other

Other1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018
CountryUnited States
CityMiami
Period4/10/184/12/18

Keywords

  • Graph Matching
  • Multiview Tracking
  • Object Association
  • Pedestrian Detection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Media Technology

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