Stochastic simulation
Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Learning to track the visual motion of contours
Artificial Intelligence - Special volume on computer vision
Deformable Kernels for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Factorization with Uncertainty
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Learning Parameterized Models of Image Motion
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Deformable Model-Based Shape and Motion Analysis from Images Using Motion Residual Error
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Continuous capture of skin deformation
ACM SIGGRAPH 2003 Papers
Keyframe-based tracking for rotoscoping and animation
ACM SIGGRAPH 2004 Papers
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Robust facial feature tracking under varying face pose and facial expression
Pattern Recognition
Spatio-temporal graphical-model-based multiple facial feature tracking
EURASIP Journal on Applied Signal Processing
Implicit Non-Rigid Structure-from-Motion with Priors
Journal of Mathematical Imaging and Vision
Activity representation using 3D shape models
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Perspective Nonrigid Shape and Motion Recovery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Dense multi-frame optic flow for non-rigid objects using subspace constraints
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Robust trajectory-space TV-L1 optical flow for non-rigid sequences
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Bilinear spatiotemporal basis models
ACM Transactions on Graphics (TOG)
Nonrigid shape and motion from multiple perspective views
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Texture replacement of garments in monocular video sequences
EGSR'06 Proceedings of the 17th Eurographics conference on Rendering Techniques
State of the Art Report on Video-Based Graphics and Video Visualization
Computer Graphics Forum
Persistent tracking of static scene features using geometry
Computer Vision and Image Understanding
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We propose a new tracking technique that is able to capture non-rigid motion by exploiting a space-time rank constraint. Most tracking methods use a prior model in order to deal with challenging local features. The model usually has to be trained on carefully hand-labeled example data before the tracking algorithm can be used. Our new model-free tracking technique can overcome such limitations. This can be achieved in redefining the problem. Instead of first training a model and then tracking the model parameters, we are able to derive trajectory constraints first, and then estimate the model. This reduces the search space significantly and allows for a better feature disambiguation that would not be possible with traditional trackers. We demonstrate that sampling in the trajectory space, instead of in the space of shape configurations, allows us to track challenging footage without use of prior models.