查询结果:   权义萍,金鑫,张蕾,杨道业.基于Mean-Shift的卡尔曼粒子滤波车辆跟踪算法研究[J].计算机应用与软件,2014,31(9):236 - 239,252.
中文标题
基于Mean-Shift的卡尔曼粒子滤波车辆跟踪算法研究
发表栏目
算法
摘要点击数
810
英文标题
ON VEHICLE TRACKING ALGORITHM USING MEAN-SHIFT-BASED KALMAN PARTICLE FILTER
作 者
权义萍 金鑫 张蕾 杨道业 Quan Yiping Jin Xin Zhang Lei Yang Daoye
作者单位
南京工业大学自动化与电气工程学院 江苏 南京 211816 南京工业大学电子与信息工程学院 江苏 南京 211816    
英文单位
College of Automation and Electrical Engineering, Nanjing University of Technology, Nanjing 211816, Jiangsu, China College of Electronic and Information Engineering,Nanjing University of Technology,Nanjing 211816, Jiangsu, China    
关键词
智能交通系统 车辆跟踪 均值漂移 粒子滤波 卡尔曼滤波器
Keywords
Intelligent transportation system Vehicle tracking Mean-shift Particle filter Kalman filter
基金项目
国家自然科学基金项目(51106070)
作者资料
权义萍,博士生,主研领域:系统工程。金鑫,硕士生。张蕾,硕士生。杨道业,博士。 。
文章摘要
在视频车辆跟踪算法中针对传统粒子滤波的非线性、非高斯性可能导致跟踪过程的不准确性,提出一种基于Mean-Shift的卡尔曼(Kalman) 粒子滤波算法。该算法利用建立基于目标颜色直方图特征模型对视频车辆目标进行建模,并将其与Kalman滤波相结合进行更新;通过采用Mean Shift算法将Kalman滤波器引用到粒子滤波器当中,通过预测迭代,从而达到对车辆的运行轨迹的修正。将先验信息预测与粒子滤波相结合在保持跟踪系统整体上的非线性、非高斯性,兼顾了卡尔曼滤波局部的线性高斯特性。实验结果表明,该方法与传统粒子滤波方法相比,具有较好的实时性和较高的准确率,能够准确稳定地对目标车辆进行跟踪。
Abstract
In video vehicle tracking algorithm, the nonlinear, non-Gaussian property in traditional particle filter may lead to inaccuracy in tracking process. This paper puts forward a mean-shift-based Kalman particle filter algorithm to solve this problem. The algorithm models the video vehicle target by making use of building the target colour-based histogram feature model, and combines it with the Kalman filter for updating; it also applies the Kalman filter to particle filter by using mean-shift algorithm, and achieves the correction of vehicles moving track through prediction iteration. The method combines the priori information and the particle filtering on the overall nonlinear and non-Gaussian properties of the tracking system, and takes into account the local linear Gaussian feature of Kalman filter as well. Experimental result shows that this method has better real-time property and higher accuracy rate than the traditional particle filter methods, and is able to track the target vehicles accurately and stably.
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