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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Camera-based perception systems play a central role in modern autonomousvehicles. These camera based perception algorithms require an accuratecalibration to map the real world distances to image pixels. In practice,calibration is a laborious procedure requiring specialised data collection andcareful tuning. This process must be repeated whenever the parameters of thecamera change, which can be a frequent occurrence in autonomous vehicles. Hencethere is a need to calibrate at regular intervals to ensure the camera isaccurate. Proposed is a deep learning framework to learn intrinsic andextrinsic calibration of the camera in real time. The framework isself-supervised and doesn't require any labelling or supervision to learn thecalibration parameters. The framework learns calibration without the need forany physical targets or to drive the car on special planar surfaces.

Self-Supervised Online Camera Calibration for Automated Driving and Parking Applications