关键词: 三维人体运动恢复, L1跟踪器, 多视图, 分层搜索, 模板更新

Abstract: While the research on 3D human motion reconstruction from video has progressed rapidly, but most approaches rely on accurate foreground silhouettes, which lose the inner texture information. This paper presents a novel method of reconstructing 3D human motion using image texture, and proposes a robust and adaptive tracker model. The model is based on L1 tracker, and can extend L1 tracker to the case of articulated body parts, in multiple view images with hierarchical searching. The tracker model can find the target with minimum reconstruction error from the template subspace, and this error in every view is taken as the likelihood measure function. The method is performed in annealed particle filter framework. To improve the tracking performance, two special methods are employed in the texture template updating process. First, 3D human body model is used to predict self-occlusion which is more accurate than image texture. Secondly, the template coefficients are used to detect bad target found by the tracker model. Combining these two detectors can avoid bringing an improper tracking result to texture template set. The proposed method shows good performance on HumanEva-II dataset.

Key words: 3D human motion reconstruction, L1 tracker, multiple view, hierarchical search, template update