A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markerless tracking of complex human motions from multiple views
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision
Multiview human pose estimation with unconstrained motions
Pattern Recognition Letters
Multi-view 3D Human Pose Estimation in Complex Environment
International Journal of Computer Vision
Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation
International Journal of Computer Vision
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We present a system for markerless human motion capture through a hierarchical method from multiple camera views. In the absence of markers, the task of recovering the human pose is challenging and requires strong image features and robust algorithm. We propose a solution which integrates the 2D posture information and the volumetric reconstruction. Firstly, the model's initia posture is obtained through the method of segmenting silhouette. After that, we track the human pose by using a hierarchical method, which is divided into three steps: head detection, torso prediction and limb matching. In order to gain the robust results, we discard the interior voxel data, use the middle voxel data for motion tracking, and use the surface voxel data for global optimization. The experiment results show that the method is valid and robust.