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arXiv · 1704.02930

Fast Learning and Prediction for Object Detection using Whitened CNN Features

Björn Barz·Erik Rodner·Christoph Käding·Joachim Denzler
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Abstract

We combine features extracted from pre-trained convolutional neural networks (CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of both: the high detection performance of CNNs, automatic feature engineering, fast model learning from few training samples and efficient sliding-window detection. The Adaptive Real-Time Object Detection System (ARTOS) has been refactored broadly to be used in combination with Caffe for the experimental studies reported in this work.

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Björn Barz, Erik Rodner, Christoph Käding, Joachim Denzler. 2017-04-12. Fast Learning and Prediction for Object Detection using Whitened CNN Features. https://arxiv.org/abs/1704.02930

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