Synthetic Ground Truth Generation for Testing, Technology Evaluation and Verification (SyntTEV)

Abstract

Nowadays, several computer devices are used to visually detect objects, people and activities. Their quality and performance depends on limited datasets created and annotated by error-prone and expensive human handwork. But to reach high quality for complex detection tasks extensive datasets with errorless annotations are needed. To overcome this dilemma we create a system for automatic generation of synthetic ground truth data to allow learning of complex detection tasks as well as testing, verification and evaluation.