ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
Free-viewpoint video of human actors
ACM SIGGRAPH 2003 Papers
Continuous capture of skin deformation
ACM SIGGRAPH 2003 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-timeHumanMotion Sensingbased on Vision-based Inverse Kinematics for Interactive Applications
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Full Body Tracking from Multiple Views Using Stochastic Sampling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Real-Time 3D Articulated Pose Tracking using Particle Filters Interacting through Belief Propagation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Human pose estimation from volume data and topological graph database
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Multi-camera tracking of articulated human motion using motion and shape cues
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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Vision-based motion capture is getting popular for acquiring human motion information in various interactive applications. To enlarge its applicability, we have been developing a vision-based motion capture system which can estimate the postures of multiple people simultaneously using multiview image analysis. Our approach is divided into the following two phases: at first, extraction, or segmentation, of each person in input multiview images; then, posture analysis for one person is applied to the segmented region of each person. The segmentation is realized in the voxel space, which is reconstructed by visual cone intersection of multiview silhouettes. Here, a graph cut algorithm is employed to achieve optimal segmentation. Posture analysis is based on a model-based approach, where a skeleton model of human figure is matched with the multiview silhouettes based on a particle filter and physical constraints on human body movement. Several experimental studies show that the proposed method acquires human postures of multiple people correctly and efficiently even when they touch each otter.