IEEE Trans Pattern Anal Mach Intell - Robust Visual Tracking Using Local Sparse Appearance Model and K-Selection.

Tópicos

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Resumo

Online learned tracking is widely used for its adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of errors during the self-updating, especially for occluded scenarios. The recent literature demonstrates that appropriate combinations of trackers can help balancing the stability and flexibility requirements. We have developed a robust tracking algorithm using a local sparse appearance model (SPT) and K-Selection. A static sparse dictionary and a dynamically updated online dictionary basis distribution are used to model the target appearance. A novel sparse representation-based voting map and a sparse constraint regularized mean-shift are proposed to track the object robustly. Besides these contributions, we also introduce a new selection based dictionary learning algorithm with a locally constrained sparse representation, called $K$-Selection. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the recent literature.

Resumo Limpo

onlin learn track wide use adapt abil handl appear chang howev introduc potenti drift problem due accumul error selfupd especi occlud scenario recent literatur demonstr appropri combin tracker can help balanc stabil flexibl requir develop robust track algorithm use local spars appear model spt kselect static spars dictionari dynam updat onlin dictionari basi distribut use model target appear novel spars representationbas vote map spars constraint regular meanshift propos track object robust besid contribut also introduc new select base dictionari learn algorithm local constrain spars represent call kselect base set comprehens experi algorithm demonstr better perform altern report recent literatur

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