| Authors |
Schuster Rene, Schulter Samuel, Poier Georg, Hirzer Martin, Birchbauer Josef, Roth Peter M., Bischof Horst, Winter Martin, Schallauer Peter |
| Appeared in |
11th IEEE Workshop on Visual Surveillance (ICCV) |
| Date |
2011 |
| Abstract |
Unusual event detection, i.e., identifying unspecified
rare/critical events, has become one of the major
challenges in visual surveillance. The main solution for
this problem is to describe local or global normalness and
to report events that do not fit to the estimated models.
The majority of existing approaches, however, is limited
to a single description (e.g., either appearance or motion)
and/or builds on inflexible (unsupervised) learning
techniques, both clearly degrading the practical
applicability. To overcome these limitations, we
demonstrate a system that is capable of extracting and
modeling several representations in parallel, while in
addition allows for user interaction within a continuous
learning setup. Novel yet intuitive concepts of result
visualization and user interaction will be presented that
allow for exploiting the underlying data. |
| Link |
TBA |
