Classifier Grids for Robust Adaptive Object Detection

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 ...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Peter M. Roth, Sabine Sternig, Helmut Grabner, Horst Bischof
• Keywords: Classifier Grid, Computer Vision, CVPR 2009, Robust Adaptive Object Detection