ARTOS Adaptive Realtime Object Detection Systemhttp://mloss.orgUpdates and additions to ARTOS Adaptive Realtime Object Detection SystemenFri, 11 Jul 2014 22:02:34 -0000ARTOS Adaptive Realtime Object Detection System 1.0<html><p>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. </p> <p>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. </p> <p>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. </p> <p>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. </p> <p>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. </p></html>Bjoern Barz, Erik RodnerFri, 11 Jul 2014 22:02:34 -0000 svmimagenetobject detection