The non-existence of general-case view-invariants
Geometric invariance in computer vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Model-based invariants for 3-D vision
International Journal of Computer Vision
First Sight: A Human Body Outline Labeling System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic analysis of the application of the cross ratio to model based vision
International Journal of Computer Vision
View-Invariant Analysis of Cyclic Motion
International Journal of Computer Vision
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Model-Based Recognition of 3D Objects from Single Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
The Representation and Recognition of Human Movement Using Temporal Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Performance Analysis and Learning Approaches for Vehicle Detection and Counting in Aerial Images
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Recognition of human body motion using phase space constraints
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Ghost: A Human Body Part Labeling System Using Silhouettes
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Inferring 3D Structure with a Statistical Image-Based Shape Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Discriminative human action recognition in the learned hierarchical manifold space
Image and Vision Computing
Viewpoint insensitive action recognition using envelop shape
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A new pose-based representation for recognizing actions from multiple cameras
Computer Vision and Image Understanding
Viewpoint insensitive posture representation for action recognition
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Recognizing activities in multiple views with fusion of frame judgments
Image and Vision Computing
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Static and temporally varying 3D invariants are proposed for capturing the spatio-temporal dynamics of a general human action to enable its representation in a compact, view-invariant manner. Two variants of the representation are presented and studied: (1) a restricted-3D version, whose theory and implementation are simple and efficient but which can be applied only to a restricted class of human action, and (2) a full-3D version, whose theory and implementation are more complex but which can be applied to any general human action. A detailed analysis of the two representations is presented. We show why a straightforward implementation of the key ideas does not work well in the general case, and present strategies designed to overcome inherent weaknesses in the approach. What results is an approach for human action modeling and recognition that is not only invariant to viewpoint, but is also robust enough to handle different people, different speeds of action (and hence, frame rate) and minor variabilities in a given action, while encoding sufficient distinction among actions. Results on 2D projections of human motion capture and on manually segmented real image sequences demonstrate the effectiveness of the approach.