SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark
We release SVIRO, a synthetic dataset for sceneries in the passenger compartment of ten different vehicles, in order to analyze machine learning-based approaches for their generalization capacities and reliability when trained on a limited number of variations (e.g. identical backgrounds and texture...
Gespeichert in:
Hauptverfasser: | , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | We release SVIRO, a synthetic dataset for sceneries in the passenger
compartment of ten different vehicles, in order to analyze machine
learning-based approaches for their generalization capacities and reliability
when trained on a limited number of variations (e.g. identical backgrounds and
textures, few instances per class). This is in contrast to the intrinsically
high variability of common benchmark datasets, which focus on improving the
state-of-the-art of general tasks. Our dataset contains bounding boxes for
object detection, instance segmentation masks, keypoints for pose estimation
and depth images for each synthetic scenery as well as images for each
individual seat for classification. The advantage of our use-case is twofold:
The proximity to a realistic application to benchmark new approaches under
novel circumstances while reducing the complexity to a more tractable
environment, such that applications and theoretical questions can be tested on
a more challenging dataset as toy problems. The data and evaluation server are
available under https://sviro.kl.dfki.de. |
---|---|
DOI: | 10.48550/arxiv.2001.03483 |