L0-regularized object representation for visual tracking

Jinshan Pan, Jongwoo Lim, Zhixun Su, Ming Hsuan Yang

Research output: Contribution to conferencePaperpeer-review

15 Citations (Scopus)


In this paper, we propose a robust visual tracking method by L0-regularized prior in a particle filter framework. In contrast to existing methods, the proposed method employs L0 norm to regularize the linear coefficients of incrementally updated linear basis. The sparsity constraint enables the tracker to effectively handle difficult challenges, such as occlusion or image corruption. To achieve realtime processing, we propose a fast and efficient numerical algorithm for solving the proposed L0-regularized model. Although it is an NP-hard problem, the proposed accelerated proximal gradient (APG) approach is guaranteed to converge to a solution quickly. Extensive experimental results on challenging video sequences demonstrate that the proposed method achieves state-of-the-art results both in accuracy and speed.

Original languageEnglish
Publication statusPublished - 2014
Event25th British Machine Vision Conference, BMVC 2014 - Nottingham, United Kingdom
Duration: 2014 Sept 12014 Sept 5


Conference25th British Machine Vision Conference, BMVC 2014
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2014. The copyright of this document resides with its authors.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'L0-regularized object representation for visual tracking'. Together they form a unique fingerprint.

Cite this