Abstract
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.
This work has been partially funded by the ERC within the starting grant Dynamic MinVIP.
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Baumann, F., Ehlers, A., Vogt, K., Rosenhahn, B. (2013). Cascaded Random Forest for Fast Object Detection. In: Kämäräinen, JK., Koskela, M. (eds) Image Analysis. SCIA 2013. Lecture Notes in Computer Science, vol 7944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38886-6_13
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DOI: https://doi.org/10.1007/978-3-642-38886-6_13
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