| Authors |
Sternig Sabine, Roth Peter M., Grabner Helmut, Bischof Horst |
| Appeared in |
Proc. Computer Vision Winter Workshop |
| Date |
2009 |
| Abstract |
In this work we present a robust object detection
system for static cameras, which is suitable for real-time
applications. Thus, the system has to cope with changes
of environmental conditions, which is realized by adaptive
on-line learning a scene specific classifier. In particular,
we apply the ideas of grid-based classification, where each
image patch corresponds to one classifier. Thus, the complexity
of the detection task is reduced and a more compact
and thus more efficient representation can be applied. The
main contribution of this paper is to introduce three learning
strategies to improve the performance of grid-based detectors:
(a) pre-selecting features to assure a more efficient
representation, (b) pre-training the positive representation,
and (c) combining off-line and on-line learning. The experimental
results on person and car detection show that these
strategies significantly improve the overall performance of
the detection system. In addition, a long-term experiment
demonstrates that the proposed system is stable over time
and can thus be applied for real-world tasks. |
| Link |
PAPER |
