The nature of statistical learning theory
The nature of statistical learning theory
Digital Pattern Recognition by Moments
Journal of the ACM (JACM)
Morphable Models for the Analysis and Synthesis of Complex Motion Patterns
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Tracking persons in monocular image sequences
Computer Vision and Image Understanding
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Learning Shape Models from Examples
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Cardboard People: A Parameterized Model of Articulated Image Motion
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Model-based tracking of self-occluding articulated objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Consistency and Coupling in Human Model Likelihoods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Inferring 3D Structure with a Statistical Image-Based Shape Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3D human pose from silhouettes by relevance vector regression
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
3D shape-encoded particle filter for object tracking and its application to human body tracking
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Monocular 3D tracking of articulated human motion in silhouette and pose manifolds
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Advances in view-invariant human motion analysis: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Towards building a 4D morphable face model
Proceedings of the SSPNET 2nd International Symposium on Facial Analysis and Animation
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Many existing systems for human body tracking are based on dynamic model-based tracking that is driven by local image features. Alternatively, within a view-based approach, tracking of humans can be accomplished by the learning-based recognition of characteristic body postures which define the spatial positions of interesting points on the human body. Recognition of body postures can be based on simple image descriptors, like the moments of body silhouettes. We present a system that combines these two approaches within a common closed-loop architecture. Central characteristics of our system are: (1) Mapping of image features into a posture space with reduced dimensionality by learning one-to-many mappings from training data by a set of parallel SVM regressions. (2) Selection of the relevant regression hypotheses by a competitive particle filter that is defined over a low-dimensional hidden state space. (3) The recognized postures are used as priors to initialize and support classical model-based tracking using a flexible articulated 2D model that is driven by local image features using a vector field approach. We present pose tracking and reconstruction results based on a combination of view-based and model-based tracking. Increased robustness and improved generalization properties are achieved even for small amounts of training data.