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Proceedings Paper

Performance prediction of vehicle detection algorithms
Author(s): P. Klausmann; Kristian Kroschel; Dieter Willersinn
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Paper Abstract

Performance prediction of computer vision algorithms is of increasing interest whenever robustness to illumination variations, shadows and different weather conditions has to be ensured. The statistical model which is presented in this contribution predicts the algorithm performance under the presence of noise, image clutter and perturbations and therefore provides an algorithm-specific measure of the underlying image quality. For the prediction of the detection performance logistic regression using covariates defined by the properties of the vehicle signatures is used. This approach provides an estimate of the probability of a single vehicle signature being detected by a given detection algorithm. To describe the relationship between background clutter and the false alarm rate of the algorithm a severity measure of the image background is presented. After the construction of the algorithm model, the probability of a vehicle signature being detected and the false alarm rate are estimated on new data. The model is evaluated and compared to the true algorithm performance.

Paper Details

Date Published: 22 October 2001
PDF: 11 pages
Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445385
Show Author Affiliations
P. Klausmann, Fraunhofer-Institut fuer Information und Datenverarbeitung (Germany)
Kristian Kroschel, Fraunhofer-Institut fuer Information und Datenverarbeitung (Germany)
Dieter Willersinn, Fraunhofer-Institut fuer Information und Datenverarbeitung (Germany)


Published in SPIE Proceedings Vol. 4379:
Automatic Target Recognition XI
Firooz A. Sadjadi, Editor(s)

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