Manifold Regularized Correlation Object Tracking

Hu, Hongwei, Ma, Bo, Shen, Jianbing and Shao, Ling (2018) Manifold Regularized Correlation Object Tracking. IEEE Transactions on Neural Networks and Learning Systems, 29 (5). pp. 1786-1795. ISSN 2162-237X

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Abstract

In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches.

Item Type: Article
Uncontrolled Keywords: target tracking,correlation,manifolds,visualization,laplace equations,training
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 29 Apr 2017 05:09
Last Modified: 22 Jul 2020 01:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/63342
DOI: 10.1109/TNNLS.2017.2688448

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