3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model based human motion tracking using probability evolutionary algorithm
Pattern Recognition Letters
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Occlusion or camera setting produces a high degree of ambiguity when estimating human body motion from monocular video sequences. Good human motion models are an important means of addressing this problem. In this work, we propose a hierarchical motion model and a motion estimation for it to estimate human motion without camera calibration and with free camera operation. The model is able to generate particles in multi-spaces and thus is able to estimate both camera view and human motion at one time. We showed the possibility of achieving 3D motion estimation for simple movements such as "walking" without camera calibration and with dynamic camera operation.