  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.
