Robust object tracking via sparse collaborative appearance model

Wei Zhong, Huchuan Lu, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

323 Citations (Scopus)


In this paper, we propose a robust object tracking algorithm based on a sparse collaborative model that exploits both holistic templates and local representations to account for drastic appearance changes. Within the proposed collaborative appearance model, we develop a sparse discriminative classifier (SDC) and sparse generative model (SGM) for object tracking. In the SDC module, we present a classifier that separates the foreground object from the background based on holistic templates. In the SGM module, we propose a histogram-based method that takes the spatial information of each local patch into consideration. The update scheme considers both the most recent observations and original templates, thereby enabling the proposed algorithm to deal with appearance changes effectively and alleviate the tracking drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.

Original languageEnglish
Article number6777566
Pages (from-to)2356-2368
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number5
Publication statusPublished - 2014 May

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Robust object tracking via sparse collaborative appearance model'. Together they form a unique fingerprint.

Cite this