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
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
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
Learning dynamics for exemplar-based gesture recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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In action recognition recently prototype-based classification methods became popular. However, such methods, even showing competitive classification results, are often limited due to too simple and thus insufficient representations and require a long-term analysis. To compensate these problems we propose to use more sophisticated features and an efficient prototype-based representation allowing for a single-frame evaluation. In particular, we apply four feature cues in parallel (two for appearance and two for motion) and apply a hierarchical k-means tree, where the obtained leaf nodes represent the prototypes. In addition, to increase the classification power, we introduce a temporal weighting scheme for the different information cues. Thus, in contrast to existing methods, which typically use global weighting strategies (i.e., the same weights are applied for all data) the weights are estimated separately for a specific point in time. We demonstrate our approach on standard benchmark datasets showing excellent classification results. In particular, we give a detailed study on the applied features, the hierarchical tree representation, and the influence of temporal weighting as well as a competitive comparison to existing state-of-the-art methods.