CAJ | 학술논문

头部姿态估计是人类行为和注意力的关键，受到光照、噪声、身份、遮挡等许多因素的影响。为了提高非约束环境下的估计准确率和鲁棒性，该论文提出了树结构分层随机森林在非约束环境下的多类头部姿态估计。首先，为了消除不同环境的噪声影响，提取人脸区域的组合纹理特征，对人脸区域进行积极人脸子区域的分类，分类结果作为树结构分层随机森林的先验知识输入；其次，提出了一种树结构分层随机森林算法，分层估计多自由度下的头部姿态；再次，为了增强算法的分类能力，使用自适应高斯混合模型作为多层次子森林叶子节点的投票模型。在多个公共数据集上的多种非约束实验环境下进行头部姿态估计，最终实验结果表明所提算法在不同质量的图像上都有很好的估计准确率和鲁棒性。
두부자태고계시인류행위화주의력적관건，수도광조、조성、신빈、차당등허다인소적영향。위료제고비약속배경하적고계준학솔화로봉성，해논문제출료수결구분층수궤삼림재비약속배경하적다류두부자태고계。수선，위료소제불동배경적조성영향，제취인검구역적조합문리특정，대인검구역진행적겁인검자구역적분류，분류결과작위수결구분층수궤삼림적선험지식수입；기차，제출료일충수결구분층수궤삼림산법，분층고계다자유도하적두부자태；재차，위료증강산법적분류능력，사용자괄응고사혼합모형작위다층차자삼림협자절점적투표모형。재다개공공수거집상적다충비약속실험배경하진행두부자태고계，최종실험결과표명소제산법재불동질량적도상상도유흔호적고계준학솔화로봉성。
Head pose estimation is an important evaluating indicator of human attention, which depends on many factors, such as illumination, noise, identification, occlusion and so on. In order to enhance estimation efficiency and accuracy, this paper presents tree-structure cascaded random forests to estimate head pose in different quality images. First, in order to eliminate the influence of different environment noise, combined texture features in random forests for positive facial patch classification are extracted, which will be the privileged inputs to estimate head pose. Second, a coarse-to-fine approach is proposed to estimate head pose both in the yaw and pitch, which is called tree-structure cascaded random forests. Third, an adaptive Gaussian mixture model is used to enhance discriminate vote energy in the tree distribution. This framework is evaluated in unconstrained environmental datasets. The experiments show that the proposed approach has a remarkable and robust performance in different quality images.