Multiperspective Automotive Labeling

Conference · Wed Jan 01 04:00:00 EST 2020

DOI:https://doi.org/10.1109/IPDPSW50202.2020.00155· OSTI ID:1804073

Jacobs, Luke; Kodumuri, Ahkil; James, Jim; Park, Seongha; Kim, Yongho

Supervised machine learning techniques inherently rely on datasets to be trained. With image datasets traditionally being annotated by humans, many advancements in image annotation tools have been made to ensure creation of rich datasets with accurate labels. Nevertheless, users still find it challenging to create and use their own datasets with labels that reflect their problem domain. We propose a streamlined labeling process that aligns multiperspective images and allows a transition from a labeled perspective to other perspectives. The main goal of this work is to reduce the human effort required for labeling vehicle images under favorable conditions where the image perspectives are correlated and one or more perspectives are known. A case study is described and analyzed to show the effectiveness of the process, as well as constraints and limitations when applied to other cases.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: USDOE Office of Science; Exelon Generation
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1804073
• Country of Publication: United States
• Language: English

Related Subjects

training dataset
multiperspective images
supervised machine learning
automotive labeling
supervised learning