Self-occlusion robust 3D human pose tracking from monocular image sequence
Self-occlusion robust 3D human pose tracking from monocular image sequence
复制标题
从单目图像序列进行自遮挡鲁棒 3D 人体姿态跟踪
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Seong
中科院分区：
文献类型：
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作者：
Nam;A. Yuille;Seong
姿态跟踪技术在无标记人体运动捕捉系统、人机交互、视频监控等领域有着广阔的应用前景。虽然在过去的几十年里，许多方法被引入，自遮挡-一个身体部分被另一个身体部分遮挡-仍然被认为是3D人体姿态跟踪的最困难的问题之一。在本文中，我们提出了一种自遮挡状态估计方法。使用马尔可夫随机场（MRF）来建模表示两个人体部位之间的成对深度顺序的遮挡状态。提出了一种新的估计方法，分别推断身体姿态和遮挡状态。HumanEva数据集用于测试所提出的方法。为了评估和量化遮挡状态变化的频率，我们标记遮挡状态的基础事实。
Pose tracking technique has great potential for many applications such as marker-free human motion capture system, Human Computer Interactions (HCI), and video surveillance. Though many methods are introduced during last decades, self-occlusion - one body part is occluded by another one - is still considered one of the most difficult problems for 3D human pose tracking. In this paper, we propose a self-occlusion state estimation method. A MRF (Markov Random Field) is used to model the occlusion state which represents the pairwise depth order between two human body parts. A novel estimation method is proposed to infer a body pose and an occlusion state separately. HumanEva dataset is used for testing the proposed method. In order to evaluate and quantify how often the occlusion state changes, we label the ground truth of occlusion state.