Tracking and data association
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Motion planning in the presence of moving obstacles
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
IEEE Transactions on Neural Networks
Adaptive human motion analysis and prediction
Pattern Recognition
A policy-blending formalism for shared control
International Journal of Robotics Research
Probabilistic movement modeling for intention inference in human-robot interaction
International Journal of Robotics Research
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Predicting motion of humans, animals and other objects which move according to internal plans is a challenging problem. Most existing approaches operate in two stages: (a) learning typical motion patterns by observing an environment and (b) predicting future motion on the basis of the learned patterns. In existing techniques, learning is performed off-line, hence, it is impossible to refine the existing knowledge on the basis of the new observations obtained during the prediction phase. We propose an approach which uses hidden Markov models (HMMs) to represent motion patterns. It is different from similar approaches because it is able to learn and predict in a concurrent fashion thanks to a novel approximate learning approach, based on the growing neural gas algorithm, which estimates both HMM parameters and structure. The found structure has the property of being a planar graph, thus enabling exact inference in linear time with respect to the number of states in the model. Our experiments demonstrate that the technique works in real-time, and is able to produce sound long-term predictions of people motion.