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Determining an appropriate range of image resolutions for appearance-based object detection and Haar-like feature extraction

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2 Author(s)
Haselhoff, A. ; Commun. Theor., Univ. of Wuppertal, Wuppertal ; Kummert, A.

This work outlines an approach to measure the influence of input pattern resolution on classification performance for appearance-based object detection algorithms. Signal theory is utilized to determine a reasonable pattern or image resolution before the time-consuming training process is considered. For this reason the energy for a given low resolution image is assessed with respect to the optimal case of high resolution. The approach is justified using an AdaBoost algorithm with Haar-like features in the context of vehicle detection. Furthermore, the transfer function of a Haar-like feature is examined in the context of the framework. Tests of classifiers, trained with different resolutions, are performed and the results are presented. These results reveal that a reasonable trade-off between computational load and classification performance can be made.

Published in:

Information Theory and Its Applications, 2008. ISITA 2008. International Symposium on

Date of Conference:

7-10 Dec. 2008