Maximum likelihood estimation for multivariate mixture observations of Markov chins
IEEE Transactions on Information Theory
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Recognition of Group Activities using Dynamic Probabilistic Networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Extraction and Clustering of Motion Trajectories in Video
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Learning activity patterns using fuzzy self-organizing neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive probabilistic graphical model for representing skills in pbd settings
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Adaptive human motion analysis and prediction
Pattern Recognition
Time of arrival predictability horizons for public bus routes
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Computational Transportation Science
A survey of techniques for incremental learning of HMM parameters
Information Sciences: an International Journal
Multi-camera tracking using a Multi-Goal Social Force Model
Neurocomputing
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Learning intentions for improved human motion prediction
Robotics and Autonomous Systems
Hi-index | 0.00 |
Modeling and predicting human and vehicle motion is an active research domain. Due to the difficulty of modeling the various factors that determine motion (e.g., internal state and perception), this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g., camera and laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use offline learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. In this paper, we present an approach where motion patterns can be learned incrementally and in parallel with prediction. Our work is based on a novel extension to hidden Markov models (HMMs)--called growing hidden Markov models--which gives us the ability to incrementally learn both the parameters and the structure of the model.