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
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Non-Negative Sparse Image Codes by Convex Programming
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
A local basis representation for estimating human pose from cluttered images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
kpose: a new representation for action recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
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In this paper, we present an efficient system for action recognition from very short sequences. For action recognition typically appearance and/or motion information of an action is analyzed using a large number of frames. This is a limitation if very fast actions (e.g., in sport analysis) have to be analyzed. To overcome this limitation, we propose a method that uses a single-frame representation for actions based on appearance and motion information. In particular, we estimate Histograms of Oriented Gradients (HOGs) for the current frame as well as for the corresponding dense flow field. The thus obtained descriptors are efficiently represented by the coefficients of a Non-negative Matrix Factorization (NMF). Actions are classified using an one-vs-all Support Vector Machine. Since the flow can be estimated from two frames, in the evaluation stage only two consecutive frames are required for the action analysis. Both, the optical flow as well as the HOGs, can be computed very efficiently. In the experiments, we compare the proposed approach to state-of-the-art methods and show that it yields competitive results. In addition, we demonstrate action recognition for real-world beach-volleyball sequences.