ARTOS -- Adaptive Real-Time Object Detection System
This system addresses the burden of data preparation for users in computer vision, but it appears incremental as it builds on existing object detection methods with a focus on usability and integration.
ARTOS tackles the problem of manual data collection and annotation for object detection by enabling model creation and tuning with minimal user effort, resulting in a system that reduces learning time through fast learning techniques.
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.