Human pose tracking using online latent structured support vector machine

Kai Lung Hua, Irawati Nurmala Sari, Mei Chen Yeh

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

1 Citation (Scopus)

Abstract

Tracking human poses in a video is a challenging problem and has numerous applications. The task is particularly difficult in realistic scenes because of several intrinsic and extrinsic factors, including complicated and fast movements, occlusions and lighting changes. We propose an online learning approach for tracking human poses using latent structured Support Vector Machine (SVM). The first frame in a video is used for training, in which body parts are initialized by users and tracking models are learned using latent structured SVM. The models are updated for each subsequent frame in the video sequence. To solve the occlusion problem, we formulate a Prize-Collecting Steiner tree (PCST) problem and use a branch-and-cut algorithm to refine the detection of body parts. Experiments using several challenging videos demonstrate that the proposed method outperforms two state-of-the-art methods.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 23rd International Conference, MMM 2017, Proceedings
EditorsGylfi Thór Gudmundsson, Shin’ichi Satoh, Laurent Amsaleg, Björn Thór Jónsson, Cathal Gurrin
PublisherSpringer Verlag
Pages626-637
Number of pages12
ISBN (Print)9783319518107
DOIs
Publication statusPublished - 2017 Jan 1
Event23rd International Conference on MultiMedia Modeling, MMM 2017 - Reykjavik, Iceland
Duration: 2017 Jan 42017 Jan 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10132 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on MultiMedia Modeling, MMM 2017
CountryIceland
CityReykjavik
Period17/1/417/1/6

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Keywords

  • Body parts
  • Human pose tracking
  • Latent structured SVM
  • Online learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hua, K. L., Sari, I. N., & Yeh, M. C. (2017). Human pose tracking using online latent structured support vector machine. In G. T. Gudmundsson, S. Satoh, L. Amsaleg, B. T. Jónsson, & C. Gurrin (Eds.), MultiMedia Modeling - 23rd International Conference, MMM 2017, Proceedings (pp. 626-637). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10132 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-51811-4_51