Robust Object Tracking Using Manifold Regularized Convolutional Neural Networks

Hongwei Hu, Bo Ma*, Jianbing Shen, Hanqiu Sun, Ling Shao, Fatih Porikli

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    45 Citations (Scopus)

    Abstract

    In visual tracking, usually only a small number of samples are labeled, and most existing deep learning based trackers ignore abundant unlabeled samples that could provide additional information for deep trackers to boost their tracking performance. An intuitive way to explain unlabeled data is to incorporate manifold regularization into the common classification loss functions, but the high computational cost may prohibit those deep trackers from practical applications. To overcome this issue, we propose a two-stage approach to a deep tracker that takes into account both labeled and unlabeled samples. The annotation of unlabeled samples is propagated from its labeled neighbors first by exploring the manifold space that these samples are assumed to lie in. Then, we refine it by training a deep convolutional neural network using both labeled and unlabeled data in a supervised manner. Online visual tracking is further carried out under the framework of particle filters with the presented manifold regularized deep model being updated every few frames. Experimental results on different tracking datasets demonstrate that our tracker outperforms most existing tracking approaches. The source code and results are available at: https://github.com/shenjianbing/MRCNNTracking.

    Original languageEnglish
    Article number8419331
    Pages (from-to)510-521
    Number of pages12
    JournalIEEE Transactions on Multimedia
    Volume21
    Issue number2
    DOIs
    Publication statusPublished - Feb 2019

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