A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Complex Human Activity Recognition for Monitoring Wide Outdoor Environments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Pictorial Structures for Object Recognition
International Journal of Computer Vision
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Unsupervised Discovery of Action Classes
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Sparse flexible models of local features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Discovering Constrained Substructures in Bayesian Trees Using the E.M. Algorithm
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
View-invariant action recognition using interest points
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Learning Structural Models in Multiple Projection Spaces
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
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We present a novel method for human motion recognition. A video sequence is represented with a sparse set of spatial and spatial-temporal features by extracting static and dynamic interest points. Our model learns a set of poses along with the dynamics of the sequence. Pose models and the model of motion dynamics are represented as a constellation of static and dynamic parts, respectively. On top of the layer of individual models we build a higher level model that can be described as "constellation of constellation models". This model encodes the spatial-temporal relationships between the dynamics of the motion and the appearance of individual poses. We test the model on a publicly available action dataset and demonstrate that our new method performs well on the classification tasks. We also perform additional experiments to show how the classification performance can be improved by increasing the number of pose models in our framework.