The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
View Invariance for Human Action Recognition
International Journal of Computer Vision
Automatic Discovery of Action Taxonomies from Multiple Views
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
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ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
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Spatial-Temporal correlatons for unsupervised action classification
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
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ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
View-invariant modeling and recognition of human actions using grammars
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Spatiotemporal salient points for visual recognition of human actions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Viewpoint insensitive actions recognition using hidden conditional random fields
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
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In this paper, we present a two-layer classification model for view-invariant human action recognition based on interest points. Training videos of every action are recorded from multiple viewpoints and represented as space-time interest points. These videos do not require temporal aligning and camera estimating. The first layer of the model is view clustering. We cluster all the videos of an action using K-Means, and break the action into several sub-actions. The second layer is Bayes classifying. We use Naïve Bayes to train the sub-classifiers for the sub-actions, and then generate an optimal classifier for the action. Unlabeled data can be recognized by the optimal classifiers, which may be single-view videos, multi-view videos, or long multi-action videos. Finally, we test our algorithm on the IXMAS dataset, and the CMU motion capture library. The experiments demonstrate that our algorithm can recognize the view-invariant actions and achieve high recognition rates.