Viewpoint Adaptation for Person Detection

Thumbnail Image

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats


Citation Stats


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 algo- rithm that allows a trained single-view person detector to be adapted to a new, distinct viewpoint. We first illustrate how a feature space trans- formation can be inferred from a known homography between the source and target viewpoints. Second, we show that a variety of trained clas- sifiers can be modified to behave as if that transformation were applied to each testing instance. The proposed algorithm is evaluated on a new synthetic multi-view dataset as well as images from the PETS 2007 and CAVIAR datasets, yielding substantial performance improvements when adapting single-view person detectors to new viewpoints while increas- ing the detector frame rate. This work has the potential to improve person detection performance for cameras at non-standard viewpoints while simplifying data collection and feature extraction





Published Version (Please cite this version)




Patrick Wang

Adjunct Assistant Professor in the Department of Electrical and Computer Engineering

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.