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Visual tracking with semi-supervised online weighted multiple instance learning

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Abstract

Adaptive discriminative tracking is a new research topic that has attracted broad attention due to its extensive application value. To take full advantage of the information about targets and their surrounding background, we propose a novel single object tracking-by-detection tracker in this paper, combining semi-supervised learning, multiple instance learning and the Bayesian theorem. The tracker uses a block-based inconsistency function of the labeled and unlabeled training samples in the selection of optimal weak classifiers during the parameter updating phase of each frame. Experimental results showed that the proposed tracker has excellent performance over other eight state-of-the-art trackers for thirteen open-access video sequences.

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Acknowledgments

This work is supported by the Basic Science Research Program through the Brain Korea 21 PLUS Project and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2013778).

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Authors and Affiliations

  1. Department of Electronics Engineering, Chonbuk National University, Jeonju, South Korea

    Zhihui Wang & Yu Lu

  2. Department of Multimedia, Mokpo National University, Jeonnam, South Korea

    Sook Yoon

  3. Institute of Remote Sensing and Earth Science, Hangzhou Normal University, Hangzhou, China

    Shan Juan Xie

  4. Division of Electronics Engineering, Chonbuk National University, Jeonju, South Korea

    Dong Sun Park

Authors
  1. Zhihui Wang
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  2. Sook Yoon
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  3. Shan Juan Xie
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  4. Yu Lu
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  5. Dong Sun Park
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Corresponding author

Correspondence to Dong Sun Park.

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Wang, Z., Yoon, S., Xie, S.J. et al. Visual tracking with semi-supervised online weighted multiple instance learning. Vis Comput 32, 307–320 (2016). https://doi.org/10.1007/s00371-015-1067-1

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