SDT: A SYNTHETIC MULTI-MODAL DATASET FOR PERSON DETECTION AND POSE CLASSIFICATION

Depth and thermal sensors are well-suited for computer vision applications that involve the continuous monitoring of people, particularly in combination. Yet there is limited research in this field and a lack of available datasets. We present a method for creating synthetic but realistic depth and thermal images that include sensor noise. We utilize this method to create the SDT dataset, which contains 40k image pairs including labels for person detection and pose classification, and is publicly available. To assess the quality our image synthesis method and the utility of SDT, we train CNNs for classification on the SDT dataset and evaluate them on real data. The CNNs achieve accuracies up to 98%, highlighting their ability to generalize from synthetic to real data.