综合颜色和纹理特征的粒子滤波人脸跟踪算法

提出了一种综合颜色和纹理特征的粒子滤波人脸跟踪算法.该方法利用粒子滤波能有效处理非线性非高斯过程和融合目标人脸多种测量信息的特性,针对人脸特征对环境变化的不同鲁棒性,综合加权颜色直方图和旋转复合小波进行人脸特征描述,将颜色和纹理特征融合到粒子滤波跟踪框架中.实验结果表明了该算法的鲁棒性、精确性和灵活性,与基于单一特征的粒子滤波跟踪方法相比,该算法稳健而有效,且对现实场景下人脸的跟踪效果准确.

Face Tracking Algorithm Combing Color and Texture Features Based on Particle Filter

In this paper, a human face tracking algorithm combing color and texture features based on particle filter is proposed. The proposed approach makes use of the characteristics of particle filter which not only can effectively deal with nonlinear and non Gaussian process but also can combine multiple face features information. Different robustness to different environments of features is considered and the weighted color histogram and rotated complex wavelet filter (RCWF) are used to describe features. Thus the color and texture features can be fused under particle filter framework to develop the new face tracking algorithm. Experimental results demonstrate that, compared with the method based on single feature, the robustness, accuracy and flexibility of the algorithm have the better effectiveness of the performance, and the experiments also show the accuracy for face tracking in real scenes.