ARTOS is the Adaptive Real-Time Object Detection System created at the Computer Vision Group of the University of Jena (Germany) by Björn Barz during a research project consulted by Erik Rodner. It was inspired by (Goering et al., ICRA, 2014) and the related system developed at UC Berkeley and UMass Lowell.
It can be used to quickly learn models for visual object detection without having to collect a set of samples manually. To make this possible, it uses ImageNet, a large image database with more than 20,000 categories. It provides an average of 300-500 images with bounding box annotations for more than 3,000 of those categories and, thus, is suitable for object detection.
The purpose of ARTOS is not limited to using those images in combination with clustering and a technique called Whitened Histograms of Orientations (WHO, Hariharan et al.) to quickly learn new models, but also includes adapting those models to other domains using in-situ images and applying them to detect objects in images and video streams.
ARTOS consists of two parts: A library (libartos) which provides all the functionality mentioned above. It is implemented in C++, but exports the important functions with a C-style procedural interface in addition to allow usage of the library with a wide range of programming languages and environments. The other part is a Graphical User Interface (PyARTOS), written in Python, which allows performing the operations of ARTOS in a comfortable way.
Please note: ARTOS is still work-in-progress. This is a first release, which still lacks some functionality we will add later. Also, there is a chance to face some bugs.
- Changes to previous version:
Initial Announcement on mloss.org.
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