|IEEE International Conference on Advanced Video and Signal-Based Surveillance
|Colorado Springs, CO, USA
|Erik Bochinski, Volker Eiselein, Thomas Sikora
|Training a Convolutional Neural Network for Multi-Class Object Detection Using Solely Virtual World Data
|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.
|multimedia analysis, Deep Learning, Convolutional Neural Network, Object Detection, Dataset Synthesis, MOCATDataset
|Electronic ISBN: 978-1-5090-3811-4
Print on Demand(PoD) ISBN: 978-1-5090-3812-1