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Fast Learning and Prediction for Object Detection using Whitened CNN Features

Abstract · Apr 10, 2017 16:15 ·

hog cnns object detection artos jena adaptive sliding cs-cv

Arxiv Abstract

  • Björn Barz
  • Erik Rodner
  • Christoph Käding
  • Joachim Denzler

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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