Fast Learning and Prediction for Object Detection using Whitened CNN Features

04/10/2017
by   Björn Barz, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset