EFFICIENT OBJECT DETECTION USING PCA MODELING AND REDUCED SET SVDD

Rudra N. Hota, Venkataramana Kini B.

Abstract

Object detection problem is traditionally tackled as two class problem. Wherein the non object classes are not precisely defined. In this paper we propose cascade of principal component modeling with associated test statistics and reduced set support vector data description for efficient object detection, both of which hinge mainly on modeling of object class training data. The PCA modeling enables quick rejection of comparatively obvious non object in initial stage of the cascade to gain computation advantage. The reduced set SVDD is applied in latter stages of cascade to classify relatively difficult images. This combination of PCA modeling and reduced set support vector data description leads to a good object detection with simple pixel features.

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Paper Citation


in Harvard Style

N. Hota R. and Kini B. V. (2008). EFFICIENT OBJECT DETECTION USING PCA MODELING AND REDUCED SET SVDD . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 222-227. DOI: 10.5220/0001079402220227


in Bibtex Style

@conference{visapp08,
author={Rudra N. Hota and Venkataramana Kini B.},
title={EFFICIENT OBJECT DETECTION USING PCA MODELING AND REDUCED SET SVDD},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={222-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001079402220227},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - EFFICIENT OBJECT DETECTION USING PCA MODELING AND REDUCED SET SVDD
SN - 978-989-8111-21-0
AU - N. Hota R.
AU - Kini B. V.
PY - 2008
SP - 222
EP - 227
DO - 10.5220/0001079402220227