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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Discriminative fusion of shape and appearance features for human pose estimation
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Discriminative fusion of shape and appearance features for human pose estimation

机译：形状和外观特征的区分性融合用于人体姿势估计

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摘要

This paper presents a method for combining the shape and appearance feature types in a discriminative learning framework for human pose estimation. We first present a new appearance descriptor that is distinctive and resilient to noise for 3D human pose estimation. We then combine the proposed appearance descriptor with a shape descriptor computed from the silhouette of the human subject using discriminative learning. Our method, which we refer to as a localized decision level fusion technique, is based on clustering the output pose space into several partitions and learning a decision level fusion model for the shape and appearance descriptors in each region. The combined shape and appearance descriptor allows complementary information of the individual feature types to be exploited, leading to improved performance of the pose estimation system. We evaluate our proposed fusion method with feature level fusion and kernel level fusion methods using a synchronized video and 3D motion dataset. Our experimental results show that the proposed feature combination method gives more accurate pose estimation than the one obtained from each individual feature type. Among the three fusion methods, our localized decision level fusion method is demonstrated to perform the best for 3D pose estimation.
机译：本文提出了一种在人为姿势估计的判别学习框架中组合形状和外观特征类型的方法。我们首先提出一种新的外观描述符，该描述符对于3D人体姿势估计而言具有独特性和抗噪性。然后，我们使用判别学习将建议的外观描述符与从人类对象的轮廓计算出的形状描述符进行组合。我们的方法（称为局部决策级融合技术）基于将输出姿势空间聚类为几个分区，并为每个区域中的形状和外观描述符学习决策级融合模型。组合的形状和外观描述符允许利用各个特征类型的互补信息，从而改善了姿势估计系统的性能。我们使用同步的视频和3D运动数据集评估我们提出的融合方法，包括特征级融合和内核级融合方法。我们的实验结果表明，与从每种单独的特征类型获得的姿势组合方法相比，所提出的特征组合方法可提供更准确的姿势估计。在这三种融合方法中，我们的局部决策级融合方法被证明对3D姿态估计性能最佳。

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