复杂运动状态的视频目标跟踪自适应切换算法

Adaptive switching algorithm for complicated motion video target tracking

针对实际环境中运动目标的状态转移模型以及随机噪声分布存在的不确定性,提出了一种适用于复杂运动状态的视频目标跟踪算法。该算法同时结合了Kalman滤波(KF)实时性好的优点,以及粒子滤波(PF)能同时处理非线性、非高斯滤波问题的优点,通过对Kalman滤波性能进行分析,定义了评价滤波性能优劣的参数并作为判断条件,实现了不同运动状态下Kalman滤波和粒子滤波自适应切换。通过实验表明该方法在目标运动状态发生显著变化时仍能够实现稳定跟踪,同时具有较高的跟踪精度。

Concerning the uncertainty of video target state transition model and random noise distribution in actual environment,an algorithm used for video target tracking in complicated movement was proposed.The algorithm adopted the advantage of Kalman Filter（KF） which was of excellent real-time quality,and the advantage of Partical Filter（PF） which could deal with non-linear and non-Gaussian filtering at the same time.By analyzing the performance of KF and making its performance parameters as a determinstic term,KF and PF could be switched adaptively.By means of test,it is suggested that the method proposed in this paper can carry out steady tracking when the target motion state changes significantly,with high tracking accuracy.