KSII Transactions on Internet and Information Systems (TIIS)

  • 제12권11호
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  • Pages.5496-5521
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  • 2018
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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Visual Tracking Using Improved Multiple Instance Learning with Co-training Framework for Moving Robot

  • Zhou, Zhiyu (School of Information Science and Technology, Zhejiang Sci-Tech University) ;
  • Wang, Junjie (School of Information Science and Technology, Zhejiang Sci-Tech University) ;
  • Wang, Yaming (School of Information Science and Technology, Zhejiang Sci-Tech University) ;
  • Zhu, Zefei (School of Mechanical Engineering, Hangzhou Dianzi University) ;
  • Du, Jiayou (School of Mechanical Engineering, Hangzhou Dianzi University) ;
  • Liu, Xiangqi (School of Mechanical Engineering, Hangzhou Dianzi University) ;
  • Quan, Jiaxin (School of Information Science and Technology, Zhejiang Sci-Tech University)
  • 투고 : 2017.08.08
  • 심사 : 2018.07.04
  • 발행 : 2018.11.30
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초록

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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참고문헌

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피인용 문헌

  1. Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning vol.2020, pp.None, 2020, https://doi.org/10.1155/2020/1574054