基于多尺度特征提取的Kalman滤波跟踪OA北大核心CSCDCSTPCD
Target tracking based on multi-scale feature extraction Kalman filter
针对波动性较大目标跟踪,传统Kalman滤波算法鲁棒性和实时性不足,提出一种基于多尺度特征提取的Kalman跟踪算法.前帧目标区域特征点匹配出后续帧目标区域特征点,并以后者特征点为中心,建立搜索区域,避免了遍历整幅后续帧图像,快速地为Kalman滤波方程状态后验值提供了稳定的观测信号和观测残差.实验证明,这种作为约束条件引入传统的Kalman滤波方程的多尺度特征提取技术,克服了传统Kalman滤波时间较长,易发散的缺陷,从而使其有着良好的收敛性.
To overcome the deficiency of the robustness and real-time performance of traditional Kalman filter used for changeable target tracking, a new Kalman algorithm based on multi-scale feature extraction was proposed. After the feature points of a frame matched ones of the follow-up frame in the target area of image, the latter feature points centroid was took as the center from which the searching area was located so as to avoid traversing the whole image. So t…查看全部>>
孔军;汤心溢;蒋敏;刘士建;李丹
江南大学物联网工程学院,江苏无锡214122中国科学院上海技术物理研究所,上海200083中国科学院研究生院,北京100049中国科学院上海技术物理研究所,上海200083江南大学物联网工程学院,江苏无锡214122
信息技术与安全科学
目标跟踪多尺度特征提取Kalman滤波收敛
target tracking multi-scale feature extraction Kalman filter convergence
《红外与毫米波学报》 2011 (5)
446-450,5
中央高校基本科研业务费专项资金(JUSR211A36),国家自然科学基金多年期重大国际合作研究项目(60910005)
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