点目标视频跟踪中的噪声自适应卡尔曼滤波器

针对卡尔曼滤波器在实用过程中所遇到的运动模型选择以及噪声给定问题,基于视频点目标的特征,提出了一种点目标视频跟踪中的噪声自适应卡尔曼滤波算法.该算法结合双步动态模型,在滤波过程中根据速度的相关系数调整运动模型参数,使运动模型更加切合实际.此外,该算法结合运动模型以及观测数据对一段时间的过程噪声进行估计,同时基于成像特性,利用单帧图像中灰度值的分布,对单次观测的观测噪声进行实时估计,实现过程噪声和观测噪声的自适应.根据在外场进行的仿真实验和实际跟踪实验结果,文中所提出的方法能够有效地保证跟踪精度.

Noise adaptive Kalman filter for video point target tracking

To overcome the difficulty of choosing the dynamic model and giving the strength of noise that applying the Kalman filter will face, this paper presents an algorithm of noise adaptive Kalman filtering for video point target tracking, based on the characteristic of video point targets. To better fit the dynamics of a video point target, the two-stage dynamic model, which can changes its form with regard to the value of correlation time constant, has been chosen as the state transition model for Kalman filtering. Moreover, the process noise is estimated according to the dynamic model and the observation data. Meanwhile, the observation noise is estimated according to the grey value distribution in each image. Then the process noise and the observation noise are adaptive. According to the outfield experimental result, the methods we proposed could effectively ensure the tracking accuracy.