The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Framework for Spatio-Temporal Video Representation & Indexing
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Probabilistic Motion Parameter Models for Human Activity Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Actions Sketch: A Novel Action Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Exploring the Space of a Human Action
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Behaviour Understanding in Video: A Combined Method
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
The Function Space of an Activity
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A comprehensive study of visual event computing
Multimedia Tools and Applications
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Dynamic events comprise of spatiotemporal atomic units. In this paper we model them using a mixture model. Events are represented using a framework based on the Mixture of Factor Analyzers (MFA) model. It is to be noted that our framework is generic and is applicable for any mixture modelling scheme. The MFA, used to demonstrate the novelty of our approach, clusters events into spatially coherent mixtures in a low dimensional space. Based the observations that, (i) events comprise of varying degrees of spatial and temporal characteristics, and (ii) the number of mixtures determines the composition of these features, a method that incorporates models with varying number of mixtures is proposed. For a given event, the relative importance of each model component is estimated, thereby choosing the appropriate feature composition. The capabilities of the proposed framework are demonstrated with an application: recognition of events such as hand gestures, activities.