Object Detection Using Synthesized Data

Web proceedings papers

Authors

Matija Buric , Goran Paulin and Marina Ivasic-Kos

Abstract

Successful object detection, using CNN, requires lots of well-annotated training data which is currently not available for action recognition in handball domain. Augmenting real-world image dataset with synthesized images is not a novel approach, but the effectiveness of the creation of such a dataset and the quantities of generated images required to improve the detection can be. Starting with relatively small training dataset, by combining traditional 3D modeling with proceduralism and optimizing generator-annotator pipeline to keep rendering and annotating time under 3 FPS, we achieved 3x better detection results, using YOLO, while only tripling the training dataset

Keywords

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