• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Visual Tracking Using Improved Multiple Instance Learning with Co-training Framework for Moving Robot


Abstract

Object detection and tracking is the basic capability of mobile robots to achieve natural human–robot interaction. In this paper, an object tracking system of mobile robot is designed and validated using improved multiple instance learning algorithm. The improved multiple instance learning algorithm which prevents model drift significantly. Secondly, in order to improve the capability of classifiers, an active sample selection strategy is proposed by optimizing a bag Fisher information function instead of the bag likelihood function, which dynamically chooses most discriminative samples for classifier training. Furthermore, we integrate the co-training criterion into algorithm to update the appearance model accurately and avoid error accumulation. Finally, we evaluate our system on challenging sequences and an indoor environment in a laboratory. And the experiment results demonstrate that the proposed methods can stably and robustly track moving object.


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Cite this article

[IEEE Style]
Zhiyu Zhou, Junjie Wang, Yaming Wang, Zefei Zhu,Jiayou Du, Xiangqi Liu and Jiaxin Quan, "Visual Tracking Using Improved Multiple Instance Learning with Co-training Framework for Moving Robot," KSII Transactions on Internet and Information Systems, vol. 12, no. 11, pp. 5496-5521, 2018. DOI: 10.3837/tiis.2018.11.018

[ACM Style]
Zhou, Z., Wang, J., Wang, Y., Du, Z. Z., Liu, X., and Quan, J. 2018. Visual Tracking Using Improved Multiple Instance Learning with Co-training Framework for Moving Robot. KSII Transactions on Internet and Information Systems, 12, 11, (2018), 5496-5521. DOI: 10.3837/tiis.2018.11.018