Fast Learning and Prediction for Object Detection using Whitened CNN Features

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.

Technical Report about the possibilities introduced with ARTOS v2, originally created March 2016

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
• ARTOS -- Adaptive Real-Time Object Detection System