Zhang Xin, Fan Guoliang
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74075, USA.
IEEE Trans Syst Man Cybern B Cybern. 2010 Aug;40(4):1034-49. doi: 10.1109/TSMCB.2010.2044240. Epub 2010 Apr 19.
This paper presents a general gait representation framework for video-based human motion estimation. Specifically, we want to estimate the kinematics of an unknown gait from image sequences taken by a single camera. This approach involves two generative models, called the kinematic gait generative model (KGGM) and the visual gait generative model (VGGM), which represent the kinematics and appearances of a gait by a few latent variables, respectively. The concept of gait manifold is proposed to capture the gait variability among different individuals by which KGGM and VGGM can be integrated together, so that a new gait with unknown kinematics can be inferred from gait appearances via KGGM and VGGM. Moreover, a new particle-filtering algorithm is proposed for dynamic gait estimation, which is embedded with a segmental jump-diffusion Markov Chain Monte Carlo scheme to accommodate the gait variability in a long observed sequence. The proposed algorithm is trained from the Carnegie Mellon University (CMU) Mocap data and tested on the Brown University HumanEva data with promising results.
本文提出了一种用于基于视频的人体运动估计的通用步态表示框架。具体而言，我们希望从单摄像头拍摄的图像序列中估计未知步态的运动学。该方法涉及两个生成模型，分别称为运动学步态生成模型（KGGM）和视觉步态生成模型（VGGM），它们分别通过一些潜在变量来表示步态的运动学和外观。提出了步态流形的概念，以捕捉不同个体之间的步态变异性，通过它可以将KGGM和VGGM集成在一起，从而可以通过KGGM和VGGM从步态外观推断出具有未知运动学的新步态。此外，还提出了一种用于动态步态估计的新粒子滤波算法，该算法嵌入了分段跳跃扩散马尔可夫链蒙特卡罗方案，以适应长时间观察序列中的步态变异性。所提出的算法是根据卡内基梅隆大学（CMU）的动作捕捉数据进行训练的，并在布朗大学的HumanEva数据上进行了测试，取得了可喜的成果。
