Abstract
Classifier grids can be seen as an adaptive but robust object detector for static cameras. Instead of using a sliding window for object detection we propose to train a separate classifier for each image location, obtaining a very specific object detector with a low false alarm rate. For each classifier corresponding to a grid element we estimate two generative representations in parallel, one describing the object's class and one describing the background. These are combined in order to obtain a discriminative model. To enable to adapt to changing environments these classifiers are learned on-line (i.e., boosting). Continuously learning (24 hours a day, 7 days a week) requires a stable system. In our method this is ensured by a fixed object representation while updating only the representation of the background. We demonstrate the stability in a long-term experiment by running the system for a whole week, which shows a stable performance over time. In addition, we compare the proposed approach to state-of-the-art methods in the field of person and car detection. In both cases we obtain competitive results.
Experimental Results
Selected Publications
- Robust object detection by classifier cubes and local verification (bib)
Sabine Sternig, Hayko Riemenschneider, Peter M. Roth, Michael Donoser, and Horst Bischof
In Proc. Workshop of the Austrian Association for Pattern Recognition, 2010 - Inverse Multiple Instance Learning for Classifier Grids (bib)
Sabine Sternig, Peter M. Roth, and Horst Bischof
In Proc. IEEE Int'l Conf. on Pattern Recognition, 2010 (Winner of the Best Paper Award) - Classifier Grids for Robust Adaptive Object Detection (bib)
Peter M. Roth, Sabine Sternig, Helmut Grabner, and Horst Bischof
In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2009 - Robust Adaptive Classifier Grids for Object Detection from Static Cameras (bib)
Sabine Sternig, Peter M. Roth, Helmut Grabner, and Horst Bischof
In Proc. Computer Vision Winter Workshop, 2009
