Publications

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication


Year(s) from:  to 
Author:
Keywords (separated by spaces):

Classifier Grids for Robust Adaptive Object Detection

P. Roth, S. Sternig, H. Grabner, and H. Bischof
In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2010

Abstract

In this paper we present an adaptive but robust object detector for static cameras by introducing classifier grids. 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.


Download in pdf format
@InProceedings{eth_biwi_00755,
  author = {P. Roth and S. Sternig and H. Grabner and and H. Bischof},
  title = {Classifier Grids for Robust Adaptive Object Detection},
  booktitle = {In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2010},
  keywords = {}
}