Rapid octree construction from image sequences
CVGIP: Image Understanding
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Tracking persons in monocular image sequences
Computer Vision and Image Understanding
3D articulated models and multiview tracking with physical forces
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Body Model Acquisition and Tracking Using Voxel Data
International Journal of Computer Vision
Model-Based Silhouette Extraction for Accurate People Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
3D human body model acquisition from multiple views
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Online Appearance Learning or 3D Articulated Human Tracking
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
3D Articulated Models and Multi-View Tracking with Silhouettes
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Consistency and Coupling in Human Model Likelihoods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Free-viewpoint video of human actors
ACM SIGGRAPH 2003 Papers
Continuous capture of skin deformation
ACM SIGGRAPH 2003 Papers
Enhancing Silhouette-Based Human Motion Capture with 3D Motion Fields
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
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
A convenient multicamera self-calibration for virtual environments
Presence: Teleoperators and Virtual Environments
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Human Motion Tracking with a Kinematic Parameterization of Extremal Contours
International Journal of Computer Vision
Accurate Human Motion Capture Using an Ergonomics-Based Anthropometric Human Model
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Fast nonparametric belief propagation for real-time stereo articulated body tracking
Computer Vision and Image Understanding
Generating 3D architectural models based on hand motion and gesture
Computers in Industry
Markerless motion capture using a single depth sensor
ACM SIGGRAPH ASIA 2009 Sketches
Human pose estimation from monocular image captures
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Whole body motion primitive segmentation from monocular video
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Marker-less 3D feature tracking for mesh-based human motion capture
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
3D hand tracking in a stochastic approximation setting
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Challenges of human behavior understanding
HBU'10 Proceedings of the First international conference on Human behavior understanding
A new pose-based representation for recognizing actions from multiple cameras
Computer Vision and Image Understanding
A multiple camera system with real-time volume reconstruction for articulated skeleton pose tracking
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Multi-view 3D Human Pose Estimation in Complex Environment
International Journal of Computer Vision
Human skeleton tracking from depth data using geodesic distances and optical flow
Image and Vision Computing
3D Human model adaptation by frame selection and shape-texture optimization
Computer Vision and Image Understanding
A Self-Training Approach for Visual Tracking and Recognition of Complex Human Activity Patterns
International Journal of Computer Vision
Multiple structured light-based depth sensors for human motion analysis: a review
IWAAL'12 Proceedings of the 4th international conference on Ambient Assisted Living and Home Care
A new hierarchical method for markerless human pose estimation
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Genetic programming extension to APF-based monocular human body pose estimation
Multimedia Tools and Applications
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We present a method for markerless tracking of complex human motions from multiple camera views. In the absence of markers, the task of recovering the pose of a person during such motions is challenging and requires strong image features and robust tracking. We propose a solution which integrates multiple image cues such as edges, color information and volumetric reconstruction. We show that a combination of multiple image cues helps the tracker to overcome ambiguous situations such as limbs touching or strong occlusions of body parts. Following a model-based approach, we match an articulated body model built from superellipsoids against these image cues. Stochastic Meta Descent (SMD) optimization is used to find the pose which best matches the images. Stochastic sampling makes SMD robust against local minima and lowers the computational costs as a small set of predicted image features is sufficient for optimization. The power of SMD is demonstrated by comparing it to the commonly used Levenberg-Marquardt method. Results are shown for several challenging sequences showing complex motions and full articulation, with tracking of 24 degrees of freedom in ≈ 1 frame per second.