Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
Probabilistic Tracking with Exemplars in a Metric Space
International Journal of Computer Vision - Marr Prize Special Issue
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICA Filters For Lighting Invariant Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Robust Tracking of Multiple People in Crowds Using Laser Range Scanners
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Evolutionary Hierarchical Time Series Clustering
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Unsupervised Clustering In Streaming Data
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Vision-based 3D object localization using probabilistic models of appearance
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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For robots operating in real-world environments, the ability to deal with dynamic entities such as humans, animals, vehicles, or other robots is of fundamental importance. The variability of dynamic objects, however, is large in general, which makes it hard to manually design suitable models for their appearance and dynamics. In this paper, we present an unsupervised learning approach to this model-building problem. We describe an exemplar-based model for representing the time-varying appearance of objects in planar laser scans as well as a clustering procedure that builds a set of object classes from given observation sequences. Extensive experiments in real environments demonstrate that our system is able to autonomously learn useful models for, e.g., pedestrians, skaters, or cyclists without being provided with external class information.