2 research outputs found
ARTOS -- Adaptive Real-Time Object Detection System
ARTOS is all about creating, tuning, and applying object detection models
with just a few clicks. In particular, ARTOS facilitates learning of models for
visual object detection by eliminating the burden of having to collect and
annotate a large set of positive and negative samples manually and in addition
it implements a fast learning technique to reduce the time needed for the
learning step.
A clean and friendly GUI guides the user through the process of model
creation, adaptation of learned models to different domains using in-situ
images, and object detection on both offline images and images from a video
stream. A library written in C++ provides the main functionality of ARTOS with
a C-style procedural interface, so that it can be easily integrated with any
other project.Comment: http://cvjena.github.io/artos
Fast Learning and Prediction for Object Detection using Whitened CNN Features
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.Comment: Technical Report about the possibilities introduced with ARTOS v2,
originally created March 201