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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Hanlin Goh , Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613
Tat-Jun Chin , Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613
ABSTRACT
We investigate the task of efficiently training classifiers to build a robust place recognition system. We advocate an approach which involves densely capturing the facades of buildings and landmarks with video recordings to greedily accumulate as much visual information as possible. Our contributions include (1) a preprocessing step to effectively exploit the temporal continuity intrinsic in the video sequences to dramatically increase training efficiency, (2) training sparse classifiers discriminatively with the resulting data using the AdaBoost principle for place recognition, and (3) methods to speed up recognition using scaled kd-trees and to perform geometric validation on the results. Compared to straightforwardly applying scene recognition methods, our method not only allows a much faster training phase, the resulting classifiers are also more accurate. The sparsity of the classifiers also ensures good potential for recognition at high frame rates. We show extensive experimental results to validate our claims.
CITATION
Hanlin Goh, Tat-Jun Chin, "Boosting descriptors condensed from video sequences for place recognition", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4563141
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