Performance prediction of vehicle detection algorithms

P. Klausmann; Kristian Kroschel; Dieter Willersinn

doi:10.1117/12.445385

22 October 2001 Performance prediction of vehicle detection algorithms

P. Klausmann, Kristian Kroschel, Dieter Willersinn

Proceedings Volume 4379, Automatic Target Recognition XI; (2001) https://doi.org/10.1117/12.445385
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

P. Klausmann, Kristian Kroschel, and Dieter Willersinn "Performance prediction of vehicle detection algorithms", Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); https://doi.org/10.1117/12.445385

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KEYWORDS

Detection and tracking algorithms

Data modeling

Performance modeling

Algorithm development

Image quality

Target detection

Visual process modeling

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