# ARTOS -- Adaptive Real-Time Object Detection System

Source: [ARTOS -- Adaptive Real-Time Object Detection System](<https://www.sota2.com/research/paper/68fa617a1e209131ce9f4504>)

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

Data updated: 2025-10-25T19:59:37.091Z

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

Published: 2014-07-10T08:02:23.000Z

## 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.

## 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/1407.2721>)

[PDF](<https://arxiv.org/pdf/1407.2721.pdf>)