Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Intelligent Self -learning Characters for Computer Games
EGUK '02 Proceedings of the 20th UK conference on Eurographics
Human action-recognition using mutual invariants
Computer Vision and Image Understanding
Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Human Carrying Status in Visual Surveillance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse flexible models of local features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
An Active Vision System for Multitarget Surveillance in Dynamic Environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Class consistent k-means: Application to face and action recognition
Computer Vision and Image Understanding
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The motion analysis of the human body is an important topic of research in computer vision devoted to detecting, tracking, and understanding people's physical behavior. This strong interest is driven by a wide spectrum of applications in various areas such as smart video surveillance. Most research in behavior (or gesture) representation focusses on view-dependent representation, and some research on view invariance considers only information from 3-D models, which is effective under considerable changes of viewpoint. This paper introduces a view-independent behavior-analysis framework based on decision fusion in which distance and view angle factors are analyzed. This is a first effort to tackle the problem of behaviors under significant changes in view angle, and a first corresponding video database is built.