English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Recently, along with interest in autonomous vehicles, the importance ofmonitoring systems for both drivers and passengers inside vehicles has beenincreasing. This paper proposes a novel in-vehicle monitoring system thecombines 3D pose estimation, seat-belt segmentation, and seat-belt statusclassification networks. Our system outputs various information necessary formonitoring by accurately considering the data characteristics of the in-vehicleenvironment. Specifically, the proposed 3D pose estimation directly estimatesthe absolute coordinates of keypoints for a driver and passengers, and theproposed seat-belt segmentation is implemented by applying a structure based onthe feature pyramid. In addition, we propose a classification task todistinguish between normal and abnormal states of wearing a seat belt usingresults that combine 3D pose estimation with seat-belt segmentation. Thesetasks can be learned simultaneously and operate in real-time. Our method wasevaluated on a private dataset we newly created and annotated. The experimentalresults show that our method has significantly high performance that can beapplied directly to real in-vehicle monitoring systems.

Integrated In-vehicle Monitoring System Using 3D Human Pose Estimation and Seat Belt Segmentation