Human Motion - Understanding, Modeling, Capture and Animation

This book constitutes the refereed proceedings of the Second Workshop on Human Motion, HumanMotion 2007, held in Rio de Janeiro, Brazil October 2007 in conjunction with ICCV 2007. The 22 revised full papers presented were carefully reviewed and selected from 38 submissions. The papers are organized in topical sections on motion capture and pose estimation, body and limb tracking and segmentation and activity recognition.

Inhalt

Motion Capture and Pose Estimation.- Marker-Less 3D Feature Tracking for Mesh-Based Human Motion Capture.- Boosted Multiple Deformable Trees for Parsing Human Poses.- Gradient-Enhanced Particle Filter for Vision-Based Motion Capture.- Multi-activity Tracking in LLE Body Pose Space.- Exploiting Spatio-temporal Constraints for Robust 2D Pose Tracking.- Efficient Upper Body Pose Estimation from a Single Image or a Sequence.- Real-Time and Markerless 3D Human Motion Capture Using Multiple Views.- Modeling Human Locomotion with Topologically Constrained Latent Variable Models.- Silhouette Based Generic Model Adaptation for Marker-Less Motion Capturing.- Body and Limb Tracking and Segmentation.- 3D Hand Tracking in a Stochastic Approximation Setting.- Nonparametric Density Estimation with Adaptive, Anisotropic Kernels for Human Motion Tracking.- Multi Person Tracking Within Crowded Scenes.- Joint Appearance and Deformable Shape for Nonparametric Segmentation.- Robust Spectral 3D-Bodypart Segmentation Along Time.- Articulated Object Registration Using Simulated Physical Force/Moment for 3D Human Motion Tracking.- An Ease-of-Use Stereo-Based Particle Filter for Tracking Under Occlusion.- Activity Recognition.- Semi-Latent Dirichlet Allocation: A Hierarchical Model for Human Action Recognition.- Recognizing Activities with Multiple Cues.- Human Action Recognition Using Distribution of Oriented Rectangular Patches.- Human Motion Recognition Using Isomap and Dynamic Time Warping.- Behavior Histograms for Action Recognition and Human Detection.- Learning Actions Using Robust String Kernels.