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.
@article{arxiv.1704.02930,
title = {Fast Learning and Prediction for Object Detection using Whitened CNN Features},
author = {Björn Barz and Erik Rodner and Christoph Käding and Joachim Denzler},
journal= {arXiv preprint arXiv:1704.02930},
year = {2017}
}
Comments
Technical Report about the possibilities introduced with ARTOS v2, originally created March 2016