| 结合卡尔曼滤波和Mean Shift的抗遮挡跟踪算法 |
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| 引用本文： | 章学静,陈禾,杨静.结合卡尔曼滤波和Mean Shift的抗遮挡跟踪算法[J].北京理工大学学报,2013,33(10):1056-1061. |
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| 作者姓名： | 章学静 陈禾 杨静 |
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| 作者单位： | 北京理工大学信息与电子学院,北京100081;北京联合大学信息学院电子工程系,北京100101;北京理工大学信息与电子学院,北京,100081;北京联合大学信息学院电子工程系,北京,100101 |
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| 基金项目： | 国家自然科学基金资助项目(61171194);北京联合大学新起点资助项目(ZK10201305) |
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| 摘 要： | 针对卡尔曼滤波和Mean Shift算法结合后对严重遮挡和遮挡后复出失效且实时性差的问题,提出一种基于卡尔曼滤波和Mean Shift动态结合的改进算法. 通过在算法中加入Bhattacharyya系数进行遮挡程度判断,并根据遮挡系数的阈值选择使用卡尔曼滤波或线性预测法更新Mean Shift迭代起点. 实验结果表明,该方法能成功实现大范围连续遮挡和目标复出情况下红外目标的跟踪,并且迭代次数和跟踪时间分别减少了9.68%和17.58%,提高了跟踪的鲁棒性和实时性.
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| 关 键 词： | 卡尔曼滤波 Mean Shift算法 遮挡判断 线性预测 实时性 |
| 收稿时间： | 6/4/2012 12:00:00 AM |
Anti-Occlusion Tracking Algorithm Combined Kalman Filter and Mean Shift |
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| ZHANG Xue-jing,CHEN He and YANG Jing.Anti-Occlusion Tracking Algorithm Combined Kalman Filter and Mean Shift[J].Journal of Beijing Institute of Technology(Natural Science Edition),2013,33(10):1056-1061. |
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| Authors: | ZHANG Xue-jing CHEN He and YANG Jing |
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| Institution: | 1.School of Information and Electronic, Beijing Institute of Technology, Beijing 100081, China;Department of Electronics Engineering, School of Information, Beijing Union University, Beijing 100101, China2.School of Information and Electronic, Beijing Institute of Technology, Beijing 100081, China3.Department of Electronics Engineering, School of Information, Beijing Union University, Beijing 100101, China |
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| Abstract: | To solve the problem of significant occlusion and failure when reappearing in combining Kalman filter and Mean Shift, a new improved method which is based on Kalman filter and Mean Shift was proposed. In the algorithm, first, the parameter of Bhattacharyya is used to scale the degree of occlusion, then Kalman filter or linear prediction was chosen to update the searching-loop point of Mean Shift according to the Bhattacharyya parameter. The experiment results indicate that the searching and tracking time can be reduced down 9.68% and 17.58%. A continuous and stable tracking results can be obtained in the situation of significant occlusion and re-appearance. |
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| Keywords: | Kalman filter Mean Shift algorithm occlusion estimation linear prediction realtime |
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