CAJ | 학술논문

为解决场景模型在快速光照变化下失效的问题，提出了一种新的前景目标分割方法。该方法共包括三个步骤。首先，利用全局光照函数建立高斯混合模型；其次，提取当前帧中的纹理、ZNCC及轮廓特征；最后，将提取到的特征分两阶段与高斯混合模型进行融合(第一阶段：融合纹理及ZNCC特征；第二阶段：融合轮廓特征)，得到最终的场景分割结果。实验结果表明：该算法具有较好的鲁棒性，并且相较于基于全局光照建模的方法具有更高的精度值及召回值。
위해결장경모형재쾌속광조변화하실효적문제，제출료일충신적전경목표분할방법。해방법공포괄삼개보취。수선，이용전국광조함수건립고사혼합모형；기차，제취당전정중적문리、ZNCC급륜곽특정；최후，장제취도적특정분량계단여고사혼합모형진행융합(제일계단：융합문리급ZNCC특정；제이계단：융합륜곽특정)，득도최종적장경분할결과。실험결과표명：해산법구유교호적로봉성，병차상교우기우전국광조건모적방법구유경고적정도치급소회치。
To address the challenging problem of robust background subtraction under sudden illumination changes, a novel foreground segmentation method based on feature fusion was proposed. The method consisted of three stages. First, a scene model through integrating the global illumination function into the framework of Gaussian mixture models was built. Second, three kinds of illumination invariant features, i. e. zero mean normalized cross- correlation (ZNCC), textures, and contours, were extracted from the current frame image. Third, the illumination invariant features were combined for foreground segmentation in two steps. Specifically, the ZNCC and textures were combined in the first step, and the contour was integrated in the second step. The experimental results show that the proposed method can effectively improve the accuracy and robustness of foreground segmentation.