一种基于优化小波特征的非线性目标跟踪算法

提出了一种基于优化小波特征的,应用于复杂背景干扰环境中的非线性目标跟踪算法。选取Gabor小波网络来表征目标的空域特性,即运用一定数量的小波构成一个集合,利用优化方法优化小波参数,从而获得稳健的Gabor小波集合来表示目标特征。运用优化的非线性粒子滤波算法,使每个粒子表示目标特征的一组估计运动参数,并通过L-M优化方法使粒子向局部峰值点移动,呈现出"多峰"的跟踪形式。实验结果表明:该算法对光照、噪声不敏感,具有较强的抗局部遮挡能力,平均跟踪误差小于一个像素,与标准的非线性粒子滤波跟踪算法相比,平均跟踪误差减小了50%。

Nonlinear target tracking method based on optimized wavelet features

For tracking in complicated environment,a nonlinear target tracking method based on optimized wavelet features is proposed.Gabor wavelet network(GWN) is used to describe the features of the object.GWN includes a set of wavelets,and each of their parameters is computed by optimization procedure.The tracking framework is based on optimized particle filter and each particle figures a set of possible motion parameters.L-M optimization is then employed to drive the particles to the local peak values,and tracking with optimized particle filters is robust and efficient as a result of multimodality.The tracking result shows that the algorithm is robust to illumination variation and noise,and it also has the strong ability of tracking under local occlusion.Compared with standard particle filter method, the average tracking error of the proposed algorithm is within 1 pixel,which has been reduced by 50%.