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

Modern crowd theories agree that collective behavior is the result of theunderlying interactions among small groups of individuals. In this work, wepropose a novel algorithm for detecting social groups in crowds by means of aCorrelation Clustering procedure on people trajectories. The affinity betweencrowd members is learned through an online formulation of the Structural SVMframework and a set of specifically designed features characterizing both theirphysical and social identity, inspired by Proxemic theory, Granger causality,DTW and Heat-maps. To adhere to sociological observations, we introduce a lossfunction (G-MITRE) able to deal with the complexity of evaluating groupdetection performances. We show our algorithm achieves state-of-the-art resultswhen relying on both ground truth trajectories and tracklets previouslyextracted by available detector/tracker systems.

Socially Constrained Structural Learning for Groups Detection in Crowd