深度信息辅助的均值漂移目标跟踪算法

Mean Shift object tracking algorithm assisted by depth cues

参考目标模型中混入的背景噪声会弱化目标特征的描述,导致目标跟踪定位误差。为减少误差,依据目标与背景处于不同深度平面的特点,提出了基于深度信息辅助的和改进的背景加权直方图的Mean Shift跟踪算法,能够有效削弱核窗口中的背景干扰信息,突出目标的颜色特征信息,并适时自适应更新核带宽,减少因目标尺寸变小时引入较多的背景干扰信息。实验结果表明该算法迭代次数更少,具有良好的跟踪定精度。

The background noise in the candidate object model diminishes the object color characteristic, and induces localiza- tion error. To reduce the error, according to the discriminative depth level between the object＇ s and the background＇ s, a Mean Shift algorithm based on depth cues assisted and corrected background-weighted histogram is proposed. The proposed algorithm can sufficiently weaken the background noisy interference in the kernel window, enhance the object＇ s color feature information, and update the kernel size adaptively in due course to reduce the distractive information in the background as the object size becomes small. Experimental result shows the proposed algorithm has fewer iteration number and good localization precision of tracking.