PWP3D: Real-Time Segmentation and Tracking of 3D Objects
International Journal of Computer Vision
Human behavior analysis from depth maps
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Performance capture of interacting characters with handheld kinects
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Motion capture of hands in action using discriminative salient points
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Parametric annealing: A stochastic search method for human pose tracking
Pattern Recognition
Multi-modal user identification and object recognition surveillance system
Pattern Recognition Letters
On-set performance capture of multiple actors with a stereo camera
ACM Transactions on Graphics (TOG)
Human limb segmentation in depth maps based on spatio-temporal Graph-cuts optimization
Journal of Ambient Intelligence and Smart Environments
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We present a markerless motion capture approach that reconstructs the skeletal motion and detailed time-varying surface geometry of two closely interacting people from multi-view video. Due to ambiguities in feature-to-person assignments and frequent occlusions, it is not feasible to directly apply single-person capture approaches to the multi-person case. We therefore propose a combined image segmentation and tracking approach to overcome these difficulties. A new probabilistic shape and appearance model is employed to segment the input images and to assign each pixel uniquely to one person. Thereafter, a single-person markerless motion and surface capture approach can be applied to each individual, either one-by-one or in parallel, even under strong occlusions. We demonstrate the performance of our approach on several challenging multi-person motions, including dance and martial arts, and also provide a reference dataset for multi-person motion capture with ground truth.