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Volume 42 Issue 10
Oct.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(10): 2146-2154.
YANG Honghong, QU Shiru, MI Xiuxiuet al. Tracking approach based on online multiple instance learning with weight distribution and multiple feature representation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(10): 2146-2154. doi: 10.13700/j.bh.1001-5965.2015.0644(in Chinese)
Citation: YANG Honghong, QU Shiru, MI Xiuxiuet al. Tracking approach based on online multiple instance learning with weight distribution and multiple feature representation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(10): 2146-2154. doi: 10.13700/j.bh.1001-5965.2015.0644(in Chinese)
shu

Tracking approach based on online multiple instance learning with weight distribution and multiple feature representation

doi: 10.13700/j.bh.1001-5965.2015.0644

  • School of Automation, Northwestern Polytechnical University, Xi'an 710072, China

Funds:  Aeronautical Science Foundation of China (2012ZC53043); Specialized Research Fund for the Doctoral Program of Higher Education of China (20096102110027); Astronautic Science and Technology Innovation Foundation (CASC201104)
  • Received Date: 30 Sep 2015
  • Publish Date: 20 Oct 2016
    Abstract
  • When most existing tracking algorithms are used, target drift problem is easy to occur under a complex environment such as occlusion, pose and illumination change. This paper proposes an online visual target tracking algorithm based on the framework of multiple instance learning (MIL) tracking. The MIL tracking algorithm cannot describe the target appearance accurately because it only uses single haar-like feature, adopts the same weight during the process of learning sample packages which contain positive samples and negative samples, and ignores the characteristic of different positive samples and negative samples having different importance to the sample bags. Therefore, this paper combines the multiple features to represent the target, constructs the classifiers, integrates the complementary characteristic of multiple features to the MIL online learning process, exploits the characteristics of complementary properties to establish more accurate target appearance model, and overcomes the problem of MIL tracking algorithm that it is insufficient to describe the target appearance. Simultaneously, the weights are assigned based on the importance of different positive samples and negative samples to the sample bags, and the tracking precision is improved. The experimental results show that the proposed algorithm can effectively handle video scene occlusions, illumination changes and scale changes with high accuracy and strong robustness. Compared with incremental learning of visual tracing (IVT), MIL and online AdaBoost (OAB) tracking algorithms, through the different challenging video sequences, the average center position error of the proposed algorithm in 5 groups of test videos is far smaller than the other three algorithms, which is only 10.14 pixel, while those of IVT, MIL and OAB algorithms are 17.99, 20.29 and 33.64 pixel, respectively.

     

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