Multiple people tracking using an appearance model based on temporal color

Hyung Ki Roh, Seong Whan Lee

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

2 Citations (Scopus)

Abstract

We present a method for the detection and tracking of multiple people totally occluded or out of sight in a scene for some period of time in image sequences. Our approach is to use time weighted color information, i.e., the temporal color, for robust medium-term people tracking. It assures our system to continuously track people moving in a group with occlusion. Experimental results show that the temporal color is more stable than shape or intensity when used in various cases.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages369-378
Number of pages10
Volume1811
ISBN (Print)3540675604, 9783540675600
DOIs
Publication statusPublished - 2000
Event1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000 - Seoul, Korea, Republic of
Duration: 2000 May 152000 May 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1811
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000
CountryKorea, Republic of
CitySeoul
Period00/5/1500/5/17

Fingerprint

Model-based
Color
Image Sequence
Period of time
Occlusion
Experimental Results
Term

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Roh, H. K., & Lee, S. W. (2000). Multiple people tracking using an appearance model based on temporal color. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1811, pp. 369-378). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1811). Springer Verlag. https://doi.org/10.1007/3-540-45482-9_37

Multiple people tracking using an appearance model based on temporal color. / Roh, Hyung Ki; Lee, Seong Whan.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1811 Springer Verlag, 2000. p. 369-378 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1811).

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

Roh, HK & Lee, SW 2000, Multiple people tracking using an appearance model based on temporal color. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1811, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1811, Springer Verlag, pp. 369-378, 1st IEEE International Workshop on Biologically Motivated Computer Vision, BMCV 2000, Seoul, Korea, Republic of, 00/5/15. https://doi.org/10.1007/3-540-45482-9_37
Roh HK, Lee SW. Multiple people tracking using an appearance model based on temporal color. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1811. Springer Verlag. 2000. p. 369-378. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-45482-9_37
Roh, Hyung Ki ; Lee, Seong Whan. / Multiple people tracking using an appearance model based on temporal color. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1811 Springer Verlag, 2000. pp. 369-378 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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