Online object tracking with sparse prototypes

Dong Wang, Huchuan Lu, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

204 Citations (Scopus)


Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking algorithm with sparse prototypes, which exploits both classic principal component analysis (PCA) algorithms with recent sparse representation schemes for learning effective appearance models. We introduce ℓ1 regularization into the PCA reconstruction, and develop a novel algorithm to represent an object by sparse prototypes that account explicitly for data and noise. For tracking, objects are represented by the sparse prototypes learned online with update. In order to reduce tracking drift, we present a method that takes occlusion and motion blur into account rather than simply includes image observations for model update. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Original languageEnglish
Article number6212358
Pages (from-to)314-325
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number1
Publication statusPublished - 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design


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