View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
A Nonlinear Method for Estimating the Projective Geometry of 3 Views
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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International Journal of Computer Vision
Recognizing Human Actions in Videos Acquired by Uncalibrated Moving Cameras
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
View invariant gesture recognition using the CSEM SwissRanger SR-2 camera
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Learning to Recognize Activities from the Wrong View Point
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
International Journal of Computer Vision
View-Invariant Action Recognition from Point Triplets
IEEE Transactions on Pattern Analysis and Machine Intelligence
View-Independent Action Recognition from Temporal Self-Similarities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
On dynamic scene geometry for view-invariant action matching
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Human action recognition from videos draws tremendous interest in the past many years. In this work, we first find that the trifocal tensor resides in a twelve dimensional subspace of the original space if the first two views are already matched and the fundamental matrix between them is known, which we refer to as subtensor. Then we use the subtensor to perform the task of action recognition under three views. We find that treating the two template views separately or not considering the correspondence relation already known between the first two views omits a lot of useful information. Experiments and datasets are designed to demonstrate the effectiveness and improved performance of the proposed approach.