Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Human Motion Signatures: Analysis, Synthesis, Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Recognizing Action at a Distance
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
Robust Appearance-based Human Action Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Efficient Visual Event Detection Using Volumetric Features
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
Non-Iterative Two-Dimensional Linear Discriminant Analysis
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition
IEEE Transactions on Image Processing
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There are multiple contributory factors taking place in an action video, e.g., person, clothing, illumination, etc. When these factors change together, conventional 1-mode analysis like PCA in action space encounters difficulties. The N- mode analysis overcomes this problem. In this paper, we propose a novel framework for recognition of actions using silhouettes based on N-mode SVD. We use the silhouette ensembles to form a 3rd order tensor comprising three modes: pixels, actions and people. Using N-mode SVD, we find the bases as well as the coefficients for the action space. For a query sequence, the resulting action-mode coefficients are compared with the learned coefficients to find the action class. Through experiments on a common database, we compare the proposed method with 1-mode PCA in appearance-base recognition of human actions and show that our method outperforms 1-mode analysis.