object detection, object recognition, learn new models
Since there are a lot of questions especially in the field of "object detection" and "object recognition" i'm going to share a recent paper/project that I have recently come across.
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. 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.
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.
- project web page: http://cvjena.github.io/artos/
- github code: https://github.com/cvjena/artos
- publication: http://arxiv.org/pdf/1407.2721.pdf
I haven't tested it yet, but it looks grate!
