ARTOS -- Adaptive Real-Time Object Detection System
Bj\"orn Barz, Erik Rodner, Joachim Denzler

TL;DR
ARTOS is an easy-to-use, real-time object detection system that simplifies model creation and adaptation with minimal manual effort, fast learning, and a user-friendly GUI, suitable for integration into various applications.
Contribution
It introduces a user-friendly GUI and fast learning techniques for quick creation and adaptation of object detection models without extensive manual data annotation.
Findings
Reduces manual annotation effort
Enables quick model adaptation to new domains
Provides fast learning for real-time detection
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Advanced Neural Network Applications · Video Surveillance and Tracking Methods
