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一种鲁棒的基于集成学习的核相关红外目标跟踪算法

谢涛 吴恩斯

谢涛, 吴恩斯. 一种鲁棒的基于集成学习的核相关红外目标跟踪算法[J]. 电子与信息学报, 2018, 40(3): 602-609. doi: 10.11999/JEIT170527
引用本文: 谢涛, 吴恩斯. 一种鲁棒的基于集成学习的核相关红外目标跟踪算法[J]. 电子与信息学报, 2018, 40(3): 602-609. doi: 10.11999/JEIT170527
XIE Tao, WU Ensi. A Robust Kernelized Correlation Tracking Algorithm for Infrared Targets Based on Ensemble Learning[J]. Journal of Electronics & Information Technology, 2018, 40(3): 602-609. doi: 10.11999/JEIT170527
Citation: XIE Tao, WU Ensi. A Robust Kernelized Correlation Tracking Algorithm for Infrared Targets Based on Ensemble Learning[J]. Journal of Electronics & Information Technology, 2018, 40(3): 602-609. doi: 10.11999/JEIT170527

一种鲁棒的基于集成学习的核相关红外目标跟踪算法

doi: 10.11999/JEIT170527
基金项目: 

教育部-中国移动科研基金(MCM20160405)

A Robust Kernelized Correlation Tracking Algorithm for Infrared Targets Based on Ensemble Learning

Funds: 

The Ministry of Education-China Mobile Research Fund Project (MCM20160405)

  • 摘要: 在红外目标跟踪中,由于目标所处的背景信息复杂多变和目标外观的显著变化,单一的分类器不足以拟合多模态的数据。该文结合核相关滤波器(KCF)将多个核相关分类器通过集成学习整合到一个框架中。利用KCF分类器具有解析解的特点平衡跟踪鲁棒性与实时性之间的矛盾,从而解决单个分类器无法处理复杂背景与显著的外观变化问题,并显著提升目标跟踪的性能与稳定性。为了验证算法的有效性,该文利用两个核相关跟踪器联合学习出1个强分类器。大量的定性定量实验表明所提的算法的跟踪性能超过传统的KCF算法,且跟踪速度也超过大多数比较算法。
  • 李少毅, 梁爽, 张凯, 等. 基于红外压缩成像的点目标跟踪方法研究[J]. 电子与信息学报, 2015, 37(7): 1639-1645. doi: 10.11999/JEIT141324.
    LI Shaoyi, LIANG Shuang, ZHANG Kai, et al. Research of infrared compressive imaging based point target tracking method[J]. Journal of Electronics Information Technology, 2015, 37(7): 1639-1645. doi: 10.11999/JEIT 141324.
    袁广林, 薛模根. 基于稀疏稠密结构表示与在线鲁棒字典学习的视觉跟踪[J]. 电子与信息学报, 2015, 2015, 37(3): 536-542. doi: 10.11999/JEIT140507.
    YUAN Guanglin and XUE Mogen. Visual tracking based on sparse dense structure representation and online robust dictionary learning[J]. Journal of Electronics Information Technology, 2015, 37(3): 536-542. doi: 10.11999/JEIT140507.
    罗会兰, 钟宝康, 孔繁胜. 带权分块压缩感知的预测目标跟踪算法[J]. 电子与信息学报, 2015, 37(5): 1160-1166. doi: 10.11999/JEIT140997.
    LUO Huilan, ZHONG Baokang, and KONG Fansheng. Tracking using weighted block compressed sensing and location prediction[J]. Journal of Electronics Information Technology, 2015, 37(5): 1160-1166. doi: 10.11999/JEIT 140997.
    ROSS D A, LIM J, LIN R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1): 125-141. doi: 10.1007/s11263-007-0075 -7.
    ZHONG W, LU H, and YANG M H. Robust object tracking via sparse collaborative appearance model[J]. IEEE Transactions on Image Processing, 2014, 23(5): 2356-2368. doi: 10.1109/TIP.2014.2313227.
    薛一哲, 王拓. 基于代价敏感 Adaboost 目标跟踪[J]. 中国图像图形学报, 2016, 21(5): 544-555.
    XUE Yizhe and WANG Tuo. Object tracking based on cost-sensitive Adaboost algorithm[J]. Chinese Journal of Image and Graphics, 2016, 21(5): 544-555.
    ZHANG K and SONG H. Real-time visual tracking via online weighted multiple instance learning[J]. Pattern Recognition, 2013, 46(1): 397-411. doi: 10.1016/j.patcog.2012.07.013.
    BABENKO B, YANG M H, and BELONGIE S. Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632. doi: 10.1109/TPAMI. 2010.226.
    HU J, LU J, and TAN Y P. Deep metric learning for visual tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 26(11): 2056-2068. doi: 10.1109/ TCSVT.2015.2477936.
    KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. Imagenet classification with deep convolutional neural networks[C]. Proceedings of the Advances in Neural Information Processing Systems, Nevada, USA, 2012: 1097-1105. doi: 10.1145/3065386.
    TANG Z, WANG S, HUO J, et al. Bayesian framework with non-local and low-rank constraint for image reconstruction [C]. Proceedings of the Journal of Physics, 2017, 787: 012008. doi: 10.1088/1742-6596/787/1/012008.
    HENRIQUES J F, CASEIRO R, MARTINS P, et al. High- speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596. doi: 10.1109/TPAMI.2014.2345390.
    LIU T, WANG G, and YANG Q. Real-time part-based visual tracking via adaptive correlation filters[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 4902-4912. doi: 10.1109/ CVPR.2015.7299124.
    MATTHEWS L, ISHIKAWA T, and BAKER S. The template update problem[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(6): 810-815. doi: 10.1109/TPAMI.2004.16.
    侯志强, 黄安奇, 余旺盛, 等. 基于局部分块和模型更新的视觉跟踪算法[J]. 电子与信息学报, 2015, 37(6): 1357-1364. doi: 10.11999/JEIT141134.
    HOU Zhiqiang, HUANG Anqi, YU Wangsheng, et al. Visual object tracking method based on local patch model and model update[J]. Journal of Electronics Information Technology, 2015, 37(6): 1357-1364. doi: 10.11999/JEIT 141134.
    薛模根, 朱虹, 袁广林. 基于在线判别式字典学习的鲁棒视觉跟踪[J]. 电子与信息学报, 2015, 37(7): 1654-1659. doi: 10.11999/JEIT141325.
    XUE Mogen, ZHU Hong, and YUAN Guanglin. Robust visual tracking based on online discrimination dictionary learning[J]. Journal of Electronics Information Technology, 2015, 37(7): 1654-1659. doi: 10.11999/JEIT 141325.
    苏巧平, 刘原, 卜英乔, 等. 基于稀疏表达的多示例学习目标追踪算法[J]. 计算机工程, 2013, 39(3): 213-217.
    SHU Qiaoping, LIU Yuan, BU Yingqiao, et al. Multi-example learning target tracking algorithm based on sparse expression [J]. Computer Engineering, 2013, 39(3): 213-217.
    VIOLA P, JONES M J, and SNOW D. Detecting pedestrians using patterns of motion and appearance[J]. International Journal of Computer Vision, 2005, 63(2): 153-161. doi: 10.1007/s11263-005-6644-8.
    KALAL Z, MIKOLAJCZYK K, and MATAS J. Tracking- learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422. doi: 10.1109/TPAMI.2011.239
    ZHANG K, ZHANG L, LIU Q, et al. Fast visual tracking via dense spatio-temporal context learning[C]. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland, 2014: 127-141. doi: 10.1007/978-3-319-10602- 1_9.
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出版历程
  • 收稿日期:  2017-05-31
  • 修回日期:  2017-12-05
  • 刊出日期:  2018-03-19

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