ENDLESS: an end-to-end framework for urban synthetic dataset generation

Abstract

Computer vision models are fundamental for smart city applications. These models enable the city to interpret visual data, obtained from sensors such as surveillance cameras, to optimize its tasks and positively impact the citizens’ lives. However, these models require ever-growing amounts of labeled data for training, which is expensive and raises ethical concerns when collected in the real world. Conversely, 3D engines and simulators allow the cheap and largescale generation of automatically annotated synthetic data. This work proposes a synthetic dataset generator for the smart cities field using the CARLA simulator. The proposed generator allows the end-to-end generation of massive datasets with a single command, which includes the simulation of city assets, such as vehicles and pedestrians, and the recording and annotation of visual data. To prove the generator’s competence, a dataset with over 300K annotated frames was generated and compared with others from the state-of-art. The comparison results show that the proposed generator is capable of producing datasets comparable to the state of the art in terms of data volume and number of annotations. It’s expected that the proposed generator could be used to create useful datasets for training and evaluating computer vision models in the smart cities area. It’s also expected that this work bring attention to the synthetic data usage for smart city models.