Human Motion - Understanding, Modeling, Capture and Animation

This LNCS volume contains the papers presented at the second Workshop on Human Motion Understanding, Modeling, Capture and Animation, which took place on October 20th, 2007, accompanying the 11th IEEE International C- ference on Computer Vision in Rio de Janeiro, Brazil. In total, 38 papers were submitted to this workshop,of which 22 papers were accepted. We were careful to ensure a high standard of quality when selecting the papers. All submissions were double-blind reviewed by at least two experts. Out of the 22 accepted papers, 10 were selected for oral presentation and 12 for posters. We thank the authors of the accepted papers for taking the reviewers' comments into account in the ?nal published versions of their papers. We thank all of the authors who submitted their work, and we trust that the reviewers' comments have been of value for their research activities. The accepted papers re?ect the state of the art in the ?eld and cover various topicsrelatedto humanmotiontrackingandanalysis.Thepapersinthisvolume have been classi?ed into three categories based on the topics they cover: human 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.