Work

Fast Learning and Prediction for Object Detection using Whitened CNN Features

Year: 2017

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1704.02930

Authors: Barz, Björn, Rodner, Erik, Käding, Christoph, Denzler, Joachim

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2613051407

Cites: 7

Cited by: 1

Related to: 10