# Fast Learning and Prediction for Object Detection using Whitened CNN Features

Source: [Fast Learning and Prediction for Object Detection using Whitened CNN Features](<https://www.sota2.com/research/paper/68fa634d1e209131ce9fde5b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:33.530Z

Authors: Bj\\"orn Barz, Erik Rodner, Christoph K\\"ading, Joachim Denzler

Published: 2017-04-10T16:15:00.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1704.02930>)

[PDF](<https://arxiv.org/pdf/1704.02930.pdf>)