by Steve DIAS DA CRUZCC-BY-NC-SA
by Steve DIAS DA CRUZLicense : CC-BY-NC-SA
SVIRO was created to investigate and benchmark machine learning approaches for application in the passenger compartment regarding common challenges of realistic engineering applications. In particular, SVIRO can be used to evaluate the generalization and robustness of machine learning models when trained on a limited number of variations. The sceneries in the different vehicle interiors were generated randomly. We partitioned the available human models, child seats and backgrounds such that one part is only used for the training images (for all the vehicles) and the other part is used for the test images. Consequently, the dataset has an intrinsic dominant background, object and texture bias: all of the images are taken in a few passenger compartments, but generalization to new, unseen, passenger compartments and child seats should be achieved. The dataset consists of 10 different vehicle interiors and 25.000 sceneries in total.
TasksDetection, Segmentation, Pose Estimation