基于尺度空间粒子滤波器的多参考#br# 直方图目标跟踪算法

在基于直方图的序列图像目标跟踪算法中,目标的直方图通常都是在跟踪初始化时从目标所在的区域获得,然而单个直方图难以适应跟踪全过程中目标的各种变化。针对事先已知目标几种典型外观的跟踪问题,提出了一种基于粒子滤波器的多直方图尺度空间跟踪算法。利用多个典型直方图的线性加权来表示目标的直方图,根据目标的当前区域估计加权系数,生成下一帧的目标概率分布图,在目标概率分布图上运用尺度空间粒子滤波器,来估计多尺度规范化Laplacian滤波函数的极值,从而实现目标的定位。通过在真实序列上与现有算法的对比,表明了此算法不仅可以适应目标的色彩和明暗变化,而且能更准确地描述目标的大小,显著提高跟踪的精度。

Visual tracking by scale space particle filter with multiple reference color histograms

In most histogram based video object tracking algorithms, an object’s histogram is typically obtained from its region in the frame where the tracking is initiated. However, single histograms are not sufficient to adapt to the object’s all possible variations. For the scenario where an object’s typical appearances are available prior to tracking, a new scale space particle filter tracking algorithm based on multiple reference histograms is proposed. We firstly employ a linear weighted method to use multiple reference histograms for producing a single histogram that is used to get the object likelihood image. Then we propose to combine the scale space theory and particle filter framework to locate objects by detecting the maximum of the multi scale normalized Laplaction filter function in the likelihood image. The comparison results with existing methods in the presented experiments on real and artificial sequences demonstrate that the proposed algorithm is able to successfully track objects whose color and brightness change drastically and rapidly, describe their size more accurately, and achieve better precision.