OSDaR23: Open Sensor Data for Rail 2023

Tagiew, Rustam; Klasek, Pavel; Tilly, Roman; Köppel, Martin; Denzler, Patrick; Neumaier, Philipp; Klockau, Tobias; Boekhoff, Martin; Schwalbe, Karsten

doi:10.1109/icrae59816.2023.10458449

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article4

Other1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

WebLabel: OpenLABEL-compliant multi-sensor labelling

Urbieta,

Mujika,

Piérola

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

Annotated datasets have become crucial for training Machine Learning (ML) models for developing Autonomous Vehicles (AVs) and their functions. Generating these datasets usually involves a complex coordination of automation and manual effort. Moreover, most available labelling tools focus on specific media types (e.g., images or video). Consequently, they cannot perform complex labelling tasks for multi-sensor setups. Recently, ASAM published OpenLABEL, a standard designed to specify an annotation format flexible enough to support the development of automated driving features and to guarantee interoperability among different systems and providers. In this work, we present WebLabel, the first multipurpose web application tool for labelling complex multi-sensor data that is fully compliant with OpenLABEL 1.0. The proposed work analyses several labelling use cases demonstrating the standard's benefits and the application's flexibility to cover various heterogeneous requirements: image labelling, multi-view video object annotation, point-cloud view-based labelling for 3D geometries and action recognition.

show abstract