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偏移校正的核空间直方图目标跟踪

王勇 陈分雄 郭红想

王勇, 陈分雄, 郭红想. 偏移校正的核空间直方图目标跟踪. 自动化学报, 2012, 38(3): 430-436. doi: 10.3724/SP.J.1004.2012.00430
引用本文: 王勇, 陈分雄, 郭红想. 偏移校正的核空间直方图目标跟踪. 自动化学报, 2012, 38(3): 430-436. doi: 10.3724/SP.J.1004.2012.00430
WANG Yong, CHEN Fen-Xiong, GUO Hong-Xiang. Kernel Spatial Histogram Target Tracking Based on Template Drift Correction. ACTA AUTOMATICA SINICA, 2012, 38(3): 430-436. doi: 10.3724/SP.J.1004.2012.00430
Citation: WANG Yong, CHEN Fen-Xiong, GUO Hong-Xiang. Kernel Spatial Histogram Target Tracking Based on Template Drift Correction. ACTA AUTOMATICA SINICA, 2012, 38(3): 430-436. doi: 10.3724/SP.J.1004.2012.00430

偏移校正的核空间直方图目标跟踪

doi: 10.3724/SP.J.1004.2012.00430
详细信息
    通讯作者:

    王勇, 博士, 中国地质大学机电学院讲师.主要研究方向为智能视频监控,模式识别. E-mail: wy112708@163.com

Kernel Spatial Histogram Target Tracking Based on Template Drift Correction

  • 摘要: 针对传统均值漂移算法中核函数直方图对目标特征描述较弱、 跟踪窗不能动态调整容易导致目标跟偏或跟丢的缺点, 提出了一种改进的均值漂移跟踪算法.为提高目标特征描述的可靠性, 采用二阶空间直方图建立目标模型,以Bhattacharyya系数作为相似性度量; 通过偏移校正更新目标区域参数建立新的目标模型; 结合边缘与角点检测选取特征点建立仿射模型实现跟踪窗的调整; 根据卡尔曼残差判断目标是否被遮挡,从而选择卡尔曼滤波或是线性预测来确定目标位置. 实验结果表明,该算法可以准确地跟踪目标,对相似背景干扰、目标大小与方向的变化以及短时遮挡具有鲁棒性.
  • [1] Fukunaga K, Hostetler L D. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1): 32-40[2] Cheng Y. Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799[3] Comaniciu D, Ranesh V, Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577[4] Comaniciu D, Ranesh V, Meer P. Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, USA: IEEE, 2000. 142-149[5] Birchfield S T, Rangarajan S. Spatiograms versus histograms for region-based tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 1158-1163[6] Zhao Q, Tao H. Object tracking using color correlogram. In: Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance. Beijing, China: IEEE, 2005. 263-270[7] Conaire C O, O'Connor N E, Smeaton A F. An improved spatiogram similarity measure for robust object localization. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Honolulu, USA: IEEE, 2007. 1069-1072[8] Li Pei-Hua. An improved mean shift algorithm for object tracking. Acta Automatica Sinica, 2007, 33(4): 347-354(李培华. 一种改进的Mean Shift跟踪算法. 自动化学报, 2007, 33(4): 347-354)[9] Porikli F, Tuzel O, Meer P. Covariance tracking using model update based on Lie algebra. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006. 728-735[10] Yan Jia, Wu Min-Yuan, Chen Shu-Zhen, Zhang Qing-Lin. Mean shift tracking with adaptive tracking window. Optics and Precision Engineering, 2009, 17(10): 2606-2611(颜佳, 吴敏渊, 陈淑珍, 张青林. 跟踪窗口自适应的Mean Shift跟踪. 光学精密工程, 2009, 17(10): 2606-2611)[11] Jia Hui-Xing, Zhang Yu-Jin. Multiple kernels based object tracking using histograms of oriented gradients. Acta Automatica Sinica, 2009, 35(10): 1283-1289(贾慧星, 章毓晋. 基于梯度方向直方图特征的多核跟踪. 自动化学报, 2009, 35(10): 1283-1289)[12] Collins R T. Mean-shift blob tracking through scale space. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison, USA: IEEE, 2003. 234-240[13] Yilmaz A. Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA: IEEE, 2007. 1-6[14] Peng Ning-Song, Yang Jie, Liu Zhi, Zhang Feng-Chao. Automatic selection of kernel-bandwidth for mean-shift object tracking. Journal of Software, 2005, 16(9): 1542-1550(彭宁嵩, 杨杰, 刘志, 张风超. Mean-Shift跟踪算法中核函数窗宽的自动选取. 软件学报, 2005, 16(9): 1542-1550)[15] Peng N S, Yang J, Liu Z. Mean shift blob tracking with kernel histogram filtering and hypothesis testing. Pattern Recognition Letters, 2005, 26(5): 605-614[16] Wang Yong, Tan Yi-Hua, Tian Jin-Wen. New tracking algorithm based on mean shift with adaptive bandwidth of kernel function. Journal of Data Acquisition and Processing, 2009, 24(6): 762-766(王勇, 谭毅华, 田金文. 基于Mean shift的核窗宽自适应目标跟踪新算法. 数据采集与处理, 2009, 24(6): 762-766)[17] Zheng Q, Chellappa R. A computational vision approach to image registration. IEEE Transactions on Image Processing, 1993, 2(3): 311-326
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出版历程
  • 收稿日期:  2010-04-19
  • 修回日期:  2011-04-10
  • 刊出日期:  2012-03-20

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