@article{barz2014artos,
    type = {article},
    key = {barz2014artos},
    title = {ARTOS -- Adaptive Real-Time Object Detection System},
    author = {Björn Barz and Erik Rodner and Joachim Denzler},
    journal = {arXiv preprint arXiv:1407.2721},
    year = {2014},
    abstract = {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.},
    code = {http://cvjena.github.io/artos/},
    groups = {adaptivelearning,lifelonglearning,sceneunderstanding},
    url = {http://arxiv.org/abs/1407.2721},
}