CAJ | 학술논문

传统连续自适应均值漂移人脸跟踪算法，仅使用了人脸的色调特征，容易受到光照及相同背景色影响，为此，提出将人脸颜色和表示线端、角点、边界相关的纹理信息相结合共同构成人脸特征的CAMSHIFT算法。首先，采用带有权重的RGB颜色空间创建归一化的人脸直方图模型；然后，在视频图像中进行目标人脸直方图投影，同时进行纹理检测，保留纹理信息的人脸颜色特征，形成概率密度分布图；最后，由Mean Shift算法从当前位置迭代寻找直至定位目标人脸。实验结果验证了该算法可以抵御同色背景和部分遮挡的影响。
전통련속자괄응균치표이인검근종산법，부사용료인검적색조특정，용역수도광조급상동배경색영향，위차，제출장인검안색화표시선단、각점、변계상관적문리신식상결합공동구성인검특정적CAMSHIFT산법。수선，채용대유권중적RGB안색공간창건귀일화적인검직방도모형；연후，재시빈도상중진행목표인검직방도투영，동시진행문리검측，보류문리신식적인검안색특정，형성개솔밀도분포도；최후，유Mean Shift산법종당전위치질대심조직지정위목표인검。실험결과험증료해산법가이저어동색배경화부분차당적영향。
Traditional Continuously Adaptive Mean Shift ( CAMSHIFT ) face tracking algorithm only use hue characteristics of the face, while easily affected by illumination and the same color background. So, face color was combined with texture information that related to line ends, corners, edges to construct facial feature, in the CAMSHIFT face tracking algorithm. Firstly, the normalized face histogram model was created with weighted RGB color space; then the tracking face histogram was projected, and the texture in the subsequent video frame image was detected, so that the probability density distribution image with face color features retaining the texture information was formed; finally, the Mean Shift algorithm iterated looking up and the target face from the current position. The experimental results show that the algorithm can withstand the effects of the same color as the background and partial occlusion.