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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We tackle the challenge of reliably and automatically localizing pedestriansin real-life conditions through overhead depth imaging at unprecedentedhigh-density conditions. Leveraging upon a combination of Histogram of OrientedGradients-like feature descriptors, neural networks, data augmentation andcustom data annotation strategies, this work contributes a robust and scalablemachine learning-based localization algorithm, which delivers near-humanlocalization performance in real-time, even with local pedestrian density ofabout 3 ped/m2, a case in which most state-of-the art algorithms degradesignificantly in performance.

Accurate pedestrian localization in overhead depth images via Height-Augmented HOG