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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)


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.

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  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 = {}