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Soft Computing

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16.05.2019 | Methodologies and Application

SCRM: self-correlated representation model for visual tracking

verfasst von: Shengqin Jiang, Xiaobo Lu, Fengna Cheng

Erschienen in: Soft Computing | Ausgabe 3/2020

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Abstract

Sparse representation (SR) as a seminal model for visual tracking explores the relationship between all candidates and the observed templates. Different from SR-based trackers, we propose a self-correlated representation model for robust visual tracking. Firstly, we learn a low-dimensional subspace representation from highly correlated templates to model the object, which aims at eliminating the redundant information and reducing the influence of noisy templates. Then, we represent the subspace by itself to learn the inner underlying features from subspace vectors. To further enhance model’s discriminating power, a new observation model is developed by considering both error distribution and large outliers. Experiments are conducted on some challenging video clips and demonstrate the favorable performance of our tracking system compared to some state-of-the-art representation-based trackers.

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Titel: SCRM: self-correlated representation model for visual tracking
verfasst von: Shengqin Jiang
Xiaobo Lu
Fengna Cheng
Publikationsdatum: 16.05.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 3/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04052-w

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