| Authors |
Mauthner Thomas, Roth Peter M., Bischof Horst |
| Appeared in |
Proc. Asian Computer Vision Conference |
| Date |
2010 |
| Abstract |
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. |
| Link |
PDF |
