Visual tracking via graph-based efficient manifold ranking with low-dimensional compressive features

Tao Zhou, Xiangjian He, Kai Xie, Keren Fu, Junhao Zhang, Jie Yang

Research output: Journal PublicationConference articlepeer-review

6 Citations (Scopus)


In this paper, a novel and robust tracking method based on efficient manifold ranking is proposed. For tracking, tracked results are taken as labeled nodes while candidate samples are taken as unlabeled nodes, and the goal of tracking is to search the unlabeled sample that is the most relevant with existing labeled nodes by manifold ranking algorithm. Meanwhile, we adopt non-adaptive random projections to preserve the structure of original image space, and a very sparse measurement matrix is used to efficiently extract low-dimensional compres-sive features for object representation. Furthermore, spatial context is used to improve the robustness to appearance variations. Experimental results on some challenging video sequences show the proposed algorithm outperforms six state-of-the-art methods in terms of accuracy and robustness.

Original languageEnglish
Article number6890194
JournalProceedings - IEEE International Conference on Multimedia and Expo
Issue numberSeptmber
Publication statusPublished - 3 Sept 2014
Externally publishedYes
Event2014 IEEE International Conference on Multimedia and Expo, ICME 2014 - Chengdu, China
Duration: 14 Jul 201418 Jul 2014


  • appearance model
  • low-dimensional compres-sive features
  • manifold ranking
  • random projections
  • spatial context
  • visual tracking

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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