Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Active shape models—their training and application
Computer Vision and Image Understanding
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Learning and Classification of Complex Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Quasi-Random Sampling for Condensation
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
Multi-Feature Hierarchical Template Matching Using Distance Transforms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Wormholes in Shape Space: Tracking through Discontinuous Changes in Shape
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Journal of Cognitive Neuroscience
A Multi-modal Particle Filter Based Motorcycle Tracking System
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
A hierarchical system for recognition, tracking and pose estimation
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
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This paper addresses the problem of multimodal shape-based object tracking with learned spatio-temporal representations. Multimodality is considered both in terms of shape representation and in terms of state propagation. Shape representation involves a set of distinct linear subspace models or Point Distribution Models (PDMs) which correspond to clusters of similar shapes. This representation is learned fully automatically from training data, without requiring prior feature correspondence. Multimodality at the state propagation level is achieved by particle filtering. The tracker uses a mixed-state: continuous parameters describe rigid transformations and shape variations within a PDM whereas a discrete parameter covers the PDM membership; discontinuous shape changes are modeled as transitions between discrete states of a Markov model. The observation density is derived from a well-behaved matching criterion involving multi-feature distance transforms. We illustrate our approach on pedestrian tracking from a moving vehicle.