@inproceedings{Goehring14:ITR, type = {inproceedings}, key = {Goehring14:ITR}, title = {Interactive Adaptation of Real-Time Object Detectors}, author = {Daniel Göhring and Judy Hoffman and Erik Rodner and Kate Saenko and Trevor Darrell}, booktitle = {International Conference on Robotics and Automation (ICRA)}, year = {2014}, pages = {1282-1289}, publisher = {IEEE}, abstract = {In the following paper, we present a framework for quickly training 2D object detectors for robotic perception. Our method can be used by robotics practitioners to quickly (under 30 seconds per object) build a large-scale real-time perception system. In particular, we show how to create new detectors on the fly using large-scale internet image databases, thus allowing a user to choose among thousands of available categories to build a detection system suitable for the particular robotic application. Furthermore, we show how to adapt these models to the current environment with just a few in-situ images. Experiments on existing 2D benchmarks evaluate the speed, accuracy, and flexibility of our system.}, groups = {adaptivelearning,sceneunderstanding,lifelonglearning}, url = {http://raptor.berkeleyvision.org/}, }