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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Cascaded Random Forest for Fast Object Detection
verfasst von : Florian Baumann, Arne Ehlers, Karsten Vogt, Bodo Rosenhahn
Erschienen in: Image Analysis
Verlag: Springer Berlin Heidelberg
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A Random Forest consists of several independent decision trees arranged in a forest. A majority vote over all trees leads to the final decision. In this paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage with minimal computational effort leading to a massively speeded-up detection framework. Three different cascade voting strategies are implemented and evaluated. Additionally, the training and classification speed-up is analyzed. Several experiments on public available datasets for pedestrian detection, lateral car detection and unconstrained face detection demonstrate the benefit of our contribution.