一种基于中心距离加权的快速相关跟踪算法

郭利生

光学与光电技术, 2017, 15 (2): 94, 网络出版: 2017-05-09

一种基于中心距离加权的快速相关跟踪算法

A Rapid Correlation Tracking Algorithm Based on Weight Distance

论文大纲

郭利生 ^*

作者单位

中国人民解放军第66242部队, 内蒙古锡林郭勒盟苏尼特右旗 011216

目标跟踪 Kalman滤波相似性度量快速搜索目标遮挡 target tracking Kalman filter similarity measurement rapid search strategy object occlusion

摘要

针对跟踪过程中目标被遮挡的问题,提出了一种基于中心距离加权的快速相关跟踪算法,定义了基于灰度差异和距离加权的相似性度量,并根据相关系数自适应更新目标模板,提高了目标的跟踪稳定性和定位精度,结合多尺度的高斯金字塔快速搜索策略,加快了目标匹配速度,在目标遮挡过程中采用Kalman滤波算法预测目标位置,目标退出遮挡时可以重新捕获。实验结果表明,该方法能较好地抑制噪声和目标遮挡的影响,目标遮挡重新捕获成功率高于85%,目标跟踪精度优于10个像素。该算法可有效地跟踪复杂地面背景的运动目标。

Abstract

To solve the occlusion problem of object tracking, a rapid correlation tracking algorithm based on weight distance is proposed in this paper. A similarity measurement is defined based on weight distance and grayscale differences between object template and the reference image area. Adaptive template update strategy which can adjust the correlation coefficient is used to improve the stability and tracking accuracy. Multi-scale Gaussian pyramid strategy is used to speed target up matching. Kalman filter is applied to predict the objects trajectory under occlusions. The experimental result show that the target can be tracked steadily using method presented in this paper. The target acquisition rate is higher than 85% and target tracking accuracy is better than 10 pixels. This algorithm can effectively track the moving targets in complex background.

参考文献

[1] 林明秀, 刘伟佳, 徐心和. 基于模板匹配的多模式车辆跟踪算法［J］. 系统仿真学报, 2007, 19(7): 1519-1522.

LIN Ming-xiu, LIU Wei-jia, XU Xin-he. Algorithm for multimode vehicle tracking based on template match［J］. Journal of System Simulation, 2007, 19(7): 1519-1522.

[2] 李正周, 赵峰. 基于梯度法的对空目标搜索与跟踪［J］. 系统仿真学报, 2007, 19(5): 1078-1081.

LI Zheng-zhou, ZHAO Feng. Air targets acquiring and tracking based on gradient algorithm［J］. Journal of System Simulation, 2007, 19(5): 1078-1081.

[3] 薛陈, 朱明, 刘春香, 等. 遮挡情况下目标跟踪算法综述［J］. 中国光学, 2009, 2(5): 388-394.

XUE Chen, ZHU Ming, LIU Chun-xiang, et al. Review of tracking algorithms under occlusions［J］. Chinese Optics, 2009, 2(5): 388-394.

[4] 王铭明, 陈涛, 王建立, 等. Mean-shift跟踪算法及其在光电跟踪系统中的应用［J］. 中国光学, 2014, 7(2): 332-338.

WANG Ming-ming, CHEN Tao, WANG Jian-li, et al. Mean-shift tracking algorithm and its application in optoelectronic tracking system［J］. Chinese Optics, 2014, 7(2): 332-338.

[5] Lowe G D. Distinctive image features from scale-invariant key points［J］. International Journal of Computer Vision, 2004, 60(2): 91-110.

[6] ZHOU Hui-yu, YUAN Yuan, SHI Chun-mei. Object tracking using SIFT features and mean shift［J］. Computer Vision and Image Understanding, 2009, 113(3): 345-352

[7] ZHU Lin, ZHOU Jie, SONG Jing-yan. Tracking multiple objects through occlusion with online sampling and position estimation［J］. Pattern Recognition, 2008, 41(8): 2447-2460.

[8] 陈金令, 苗东, 康博, 等. 基于Kalman滤波和模板匹配的目标跟踪技术研究［J］. 光学与光电技术, 2014, 12(6): 9-12.

CHEN Jin-ling, MIAO Dong, KANG Bo, et al. Research of target tracking based on kalman filtering and template matching［J］. Optics & Optoelectronic Technology, 2014, 12(6): 9-12

[9] Babenko B, Yang M, Belongie S. Robust object tracking with online multiple instance learning［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 33(8): 1619-1632.

[10] 郑存红. 复杂背景下相关跟踪算法的研究［J］. 光学与光电技术, 2008, 5(1): 52-54.

ZHENG Cun-hong. Correlation tracking algorithms in complex background［J］. Optics & Optoelectronic Technology, 2008, 5(1): 52-54.

[11] 李沫, 印力承, 闫天翼. 基于积分直方图的粒子滤波跟踪算法［J］. 光学与光电技术, 2013, 11(3): 45-48.

LI Mo, YIN Li-cheng, YAN Tian-yi. Particle filter tracking algorithm based on integral histogram and improved resampling［J］. Optics & Optoelectronic Technology, 2013, 11(3): 45-48.

郭利生. 一种基于中心距离加权的快速相关跟踪算法[J]. 光学与光电技术, 2017, 15(2): 94. GUO Li-sheng. A Rapid Correlation Tracking Algorithm Based on Weight Distance[J]. OPTICS & OPTOELECTRONIC TECHNOLOGY, 2017, 15(2): 94.

一种基于中心距离加权的快速相关跟踪算法

关于本站 Cookie 的使用提示

全站搜索

一种基于中心距离加权的快速相关跟踪算法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索