  An object detector performs suboptimally when applied to image data taken
from a viewpoint different from the one with which it was trained. In this
paper, we present a viewpoint adaptation algorithm that allows a trained
single-view object detector to be adapted to a new, distinct viewpoint. We
first illustrate how a feature space transformation can be inferred from a
known homography between the source and target viewpoints. Second, we show that
a variety of trained classifiers can be modified to behave as if that
transformation were applied to each testing instance. The proposed algorithm is
evaluated on a person detection task using images from the PETS 2007 and CAVIAR
datasets, as well as from a new synthetic multi-view person detection dataset.
It yields substantial performance improvements when adapting single-view person
detectors to new viewpoints, and simultaneously reduces computational
complexity. This work has the potential to improve detection performance for
cameras viewing objects from arbitrary viewpoints, while simplifying data
collection and feature extraction.
