ARTOS is the Adaptive Real-Time Object Detection System, created at the University of Jena (Germany). 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.