IEEE Trans Image Process - Online object tracking with sparse prototypes.

Tópicos

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Resumo

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 l(1) 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.

Resumo Limpo

onlin object track challeng problem entail learn effect model account appear chang caus intrins extrins factor paper propos novel onlin object track algorithm spars prototyp exploit classic princip compon analysi pca algorithm recent spars represent scheme learn effect appear model introduc l regular pca reconstruct develop novel algorithm repres object spars prototyp account explicit data nois track object repres spars prototyp learn onlin updat order reduc track drift present method take occlus motion blur account rather simpli includ imag observ model updat qualit quantit evalu challeng imag sequenc demonstr propos track algorithm perform favor sever stateoftheart method

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