Key strategies for synthetic data generation for training intelligent systems based on people detection from omnidirectional cameras

Abstract

To train Deep Neural Networks (DNNs)-based methods, suitable training data are key to help DNNs learn appropriate pattern recognition features. The use of synthetic data may help in generating sufficient and balanced data. However, models trained with such data often present a domain gap when applied to real-world scenarios. Many studies focus on techniques such as domain adaptation to minimize this gap, but little attention is paid to the data generation itself. Our work shows that this gap can be minimized by enhancing the generated data features. More specifically, we generate different synthetic training datasets with particular features and use them to train a DNN for people detection in large spaces using omnidirectional cameras. Experimental results with real-world data show that proper synthetic data minimize the domain gap. We also show that expanding a training dataset to include synthetic samples in addition to real samples, can improve the model’s capabilities.