基于ICA分布模型的粒子滤波跟踪算法

Particle Filter Tracker Based on ICA Distribution Models

为了在复杂背景、部分遮挡和光照变化等因素干扰的情况下鲁棒地跟踪视频序列中感兴趣的运动目标,提出了一种改进的粒子滤波跟踪算法。该算法针对颜色信息在目标表述中存在的不足,首先对观测模型进行改进,提出了一种基于ICA特征分布的目标模型,将基于核函数的目标特征描述转换到ICA特征空间,由于光照变化引起灰度变化经ICA后仍是同一分量,因此能有效的适应光照变化,不仅考虑并充分利用了空间信息。有效的解决了光照变化及背景颜色相近造成的目标丢失现象,提高了目标跟踪算法的鲁棒性。同时,考虑到粒子的退化现象,将均值平移算法嵌入到粒子滤波的跟踪框架中,待各粒子经过系统传播后,利用均值平移算法使粒子向其领域局部极大值处移动,使得粒子集中在测量模型的局部区域内,只需少量的粒子就覆盖了尽可能的目标分布,很好地克服了粒子滤波器的退化现象并有效缩短了计算时间,提高了目标跟踪算法的准确性和系统的实时性。实验表明,该算法不仅能在复杂背景下准确的跟踪目标,而且在光线变化和部分遮挡情况下也能保证不丢失目标。

In order to robustly track the interested object in video sequence in the presence of cluttered background,partial occlusion and illumination variations,an improved particle-filtering tracking algorithm is proposed.The feature description is transformed into the feature space of ICA（independent component analysis） from color histograms.Because illuminated change is the same components by ICA transformation.The method can reduce the influence by changes in scene illumination or similar color regions,and improve the robustness of the tracker.What＇s more,According to particles impoverishment phenomenon,the proposed method embeds mean-shift into the tracking frame of the particle filter algorithm.After propagated by system,each sample are herded to local max of its neighborhood,it require fewer particles to maintain multiple hypotheses and tackle the impoverished particles problem,at the same time, the computation time is efficiently reduced.It ensures performance simultaneously and accuracy.The experimental results show that the proposed method can effectively solve the tracking problem like illumination changes,partial occlusions and background scene changes.