Kernel Spatial Histogram Target Tracking Based on Template Drift Correction
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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.Abstract: Aiming at the limitations of the traditional mean shift, such as invariable kernel bandwidth, inadequate color distribution representation of target and the accumulative tracking errors, an improved tracking algorithm with the following strategies is proposed. The target model and the candidate are described by a modified second-order spatial histogram including color and spatial information, and the similarity between them is evaluated by Bhattacharyya coefficient. According to the target region parameter resulted from template drift correction which can eliminate the tracking errors, the target model can be estimated repeatedly. The tracking region parameters are updated through an affine transform combining corner detection and edge detection. Besides, the target motion is predicted by either Kalman filter or linear filter according to the Kalman residual error. Experimental results show that the proposed algorithm is robust against similarity distraction, scale and orientation variations and short-term occlusion.
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Key words:
- Target tracking /
- mean shift /
- spatial histogram /
- drift correction
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[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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