| Authors |
Roth Peter M., Leistner Christian, Armin Berger, Bischof Horst |
| Appeared in |
Proc. IEEE Workshop on Camera Networks |
| Date |
2010 |
| Abstract |
Recently, combining information from multiple cameras
has shown to be very beneficial for object detection and
tracking. In contrast, the goal of this work is to train detectors
exploiting the vast amount of unlabeled data given by
geometry information of a specific multiple camera setup.
Starting from a small number of positive training samples,
we apply a co-training strategy in order to generate new
very valuable samples from unlabeled data that could not be
obtained otherwise. To compensate for unreliable updates
and to increase the detection power, we introduce a new online
multiple instance co-training algorithm. The approach,
although not limited to this application, is demonstrated for
learning a person detector on different challenging scenarios.
In particular, we give a detailed analysis of the learning
process and show that by applying the proposed approach
we can train state-of-the-art person detectors. |
| Link |
PDF |
