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

MoCap-based human identification, as a pattern recognition discipline, can beoptimized using a machine learning approach. Yet in some applications such asvideo surveillance new identities can appear on the fly and labeled data forall encountered people may not always be available. This work introduces theconcept of learning walker-independent gait features directly from raw jointcoordinates by a modification of the Fisher Linear Discriminant Analysis withMaximum Margin Criterion. Our new approach shows not only that these featurescan discriminate different people than who they are learned on, but also thatthe number of learning identities can be much smaller than the number ofwalkers encountered in the real operation.

Walker-Independent Features for Gait Recognition from Motion Capture Data