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
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
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
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
International Journal of Robotics Research
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Video activity recognition in the real world
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
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
Reducing the time complexity of the fuzzy c-means algorithm
IEEE Transactions on Fuzzy Systems
Comparative analysis of human motion trajectory prediction using minimum variance curvature
Proceedings of the 6th international conference on Human-robot interaction
Optimizing motion of robotic manipulators in interaction with human operators
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
Control of robotic systems for safe interaction with human operators
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Incremental behavior modeling and suspicious activity detection
Pattern Recognition
Probabilistic movement modeling for intention inference in human-robot interaction
International Journal of Robotics Research
Hi-index | 0.00 |
Modeling and predicting human and vehicle motion is an active research domain. Owing to the difficulty in modeling the various factors that determine motion (e.g. internal state, 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, 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, called growing hidden Markov models, which gives us the ability to learn incrementally both the parameters and the structure of the model. The proposed approach has been evaluated using synthetic and real trajectory data. In our experiments our approach consistently learned motion models that were more compact and accurate than those produced by two other state-of-the-art techniques.