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

We propose a novel and pragmatic framework for traffic scene perception withroadside cameras. The proposed framework covers a full-stack of roadsideperception pipeline for infrastructure-assisted autonomous driving, includingobject detection, object localization, object tracking, and multi-camerainformation fusion. Unlike previous vision-based perception frameworks relyupon depth offset or 3D annotation at training, we adopt a modular decouplingdesign and introduce a landmark-based 3D localization method, where thedetection and localization can be well decoupled so that the model can beeasily trained based on only 2D annotations. The proposed framework applies toeither optical or thermal cameras with pinhole or fish-eye lenses. Ourframework is deployed at a two-lane roundabout located at Ellsworth Rd. andState St., Ann Arbor, MI, USA, providing 7x24 real-time traffic flow monitoringand high-precision vehicle trajectory extraction. The whole system runsefficiently on a low-power edge computing device with all-component end-to-enddelay of less than 20ms.

Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras