@INPROCEEDINGS{1491Bochinski2016,
	AUTHOR = {Erik Bochinski and Volker Eiselein and Thomas Sikora},
	TITLE = {Training a Convolutional Neural Network for Multi-Class Object Detection Using Solely Virtual World Data},
	BOOKTITLE = {IEEE International Conference on Advanced Video and Signal-Based Surveillance},
	YEAR = {2016},
	MONTH = aug,
	PAGES = {278--285},
	ADDRESS = {Colorado Springs, CO, USA},
	NOTE = {Electronic ISBN: 978-1-5090-3811-4
Print on Demand(PoD) ISBN: 978-1-5090-3812-1
DOI: 10.1109/AVSS.2016.7738056},
	PDF = {http://elvera.nue.tu-berlin.de/files/1491Bochinski2016.pdf},
	ABSTRACT = {Convolutional neural networks are a popular choice for current object detection and classification systems. Their performance improves constantly but for effective training, large, hand-labeled datasets are required. We address the problem of obtaining customized, yet large enough datasets for CNN training by synthesizing them in a virtual world, thus eliminating the need for tedious human interaction for ground truth creation. We developed a CNN-based multi-class detection system that was trained solely on virtual world data and achieves competitive results compared to state-of-the-art detection systems.}
}