吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 281-287.doi: 10.13229/j.cnki.jdxbgxb201701041

• 论文 • 上一篇    下一篇

遮挡环境下多示例学习分块目标跟踪

才华1, 陈广秋1, 刘广文1, 程帅1, 于化东2   

  1. 1.长春理工大学 电子信息工程学院,长春 130022;
    2.长春理工大学 机电工程学院,长春 130022
  • 收稿日期:2016-02-21 出版日期:2017-01-20 发布日期:2017-01-20
  • 通讯作者: 陈广秋(1977-),男,副教授,博士.研究方向:图像(序列)配准与融合.E-mail:guangqiu_chen@126.com
  • 作者简介:才华(1977-),男,副教授,博士.研究方向:图像处理与机器视觉.E-mail:caihua@cust.edu.cn
  • 基金资助:
    吉林省科技发展计划项目(20130101179JC); 教育部留学基金委留学归国人员科研启动基金(教外师留1685).

Novelty fragments-based target tracking with multiple instance learning under occlusions

CAI Hua1, CHEN Guang-qiu1, LIU Guang-wen1, CHENG Shuai1, YU Hua-dong2   

  1. 1.School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022,China;
    2.College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun 130022,China
  • Received:2016-02-21 Online:2017-01-20 Published:2017-01-20

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摘要： 针对跟踪过程中遮挡导致错误累积而产生目标漂移甚至目标丢失的问题,提出多示例学习分块目标跟踪方法。该方法以随机蕨为基础检测器,通过多示例学习在线更新检测器,提高检测器对目标变化的适应能力及学习的准确性。将目标均匀分成多个子块,选取部分子块作为候选集合,每个候选块分配一个检测器,利用检测器完成每个候选块的跟踪,根据候选块的信息确定目标最终位置。实时检测候选块的有效性,替换无效的候选块,提高跟踪的鲁棒性。在目标被遮挡等复杂条件下,与目前主流跟踪算法进行了对比实验,结果表明该算法能够有效解决目标漂移甚至跟踪丢失问题,具有更高的跟踪精确度及更好的鲁棒性。

关键词: 信息处理技术, 随机蕨分类器, 多示例学习, 分块, 无效子块更换

Abstract: To solve the problem that tracking algorithm may lead to drift or failure due to the accumulated error under the occlusion environment, a Multiple instance learning based Fragment Tracker (MFT) is proposed. In this MFT, the random ferns is used as the basic detector. To improve the adaption of the target change and the precision of the learning, the multiple instance learning is introduced to online update the detector. The object is segmented into fragments and parts of them are selected as the candidate set. The candidate block is tracked by the corresponding detector. The object can be finally located by the selected blocks. A real-time valid detection is made for the candidate blocks and the invalid block is replaced with an appropriate block to improve the robustness of the tracking. Experiments on variant challenging image sequence in the occlusion environment were carried out. Results show that, compared with the state-of-art trackers, the proposed MFT solves the problem of target drift and failure efficiently and has higher accuracy and better robust.

Key words: information processing, random ferns detector, multiple instance learning, fragment, invalid block replacement

中图分类号: 

  • TP391.41
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