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Visual object tracking with online sample selection via lasso regularization

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Abstract

In the past years, discriminative methods are popular in visual tracking. The main idea of the discriminative method is to learn a classifier to distinguish the target from the background. The key step is the update of the classifier. Usually, the tracked results are chosen as the positive samples to update the classifier, which results in the failure of the updating of the classifier when the tracked results are not accurate. After that the tracker will drift away from the target. Additionally, a large number of training samples would hinder the online updating of the classifier without an appropriate sample selection strategy. To address the drift problem, we propose a score function to predict the optimal candidate directly instead of learning a classifier. Furthermore, to solve the problem of a large number of training samples, we design a sparsity-constrained sample selection strategy to choose some representative support samples from the large number of training samples on the updating stage. To evaluate the effectiveness and robustness of the proposed method, we implement experiments on the object tracking benchmark and 12 challenging sequences. The experiment results demonstrate that our approach achieves promising performance.

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Acknowledgements

This work is supported by the National Nature Science Foundation of China (No. 61402122), the 2014 Ph.D. Recruitment Program of Guizhou Normal University, the Outstanding Innovation Talents of Science and Technology Award Scheme of Education Department in Guizhou Province (Qianjiao KY word [2015]487).

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Authors and Affiliations

  1. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China

    Qiao Liu & Weihua Ou

  2. School of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Xiao Ma

  3. Key Lab of Ministry of Education for Broad Band Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

    Quan Zhou

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  1. Qiao Liu
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  2. Xiao Ma
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  3. Weihua Ou
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  4. Quan Zhou
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Corresponding author

Correspondence to Weihua Ou.

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Liu, Q., Ma, X., Ou, W. et al. Visual object tracking with online sample selection via lasso regularization. SIViP 11, 881–888 (2017). https://doi.org/10.1007/s11760-016-1035-x

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  • DOI: https://doi.org/10.1007/s11760-016-1035-x

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