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

Activity detection is one of the attractive computer vision tasks to exploitthe video streams captured by widely installed cameras. Although achievingimpressive performance, conventional activity detection algorithms are usuallydesigned under certain constraints, such as using trimmed and/orobject-centered video clips as inputs. Therefore, they failed to deal with themulti-scale multi-instance cases in real-world unconstrained video streams,which are untrimmed and have large field-of-views. Real-time requirements forstreaming analysis also mark brute force expansion of them unfeasible.  To overcome these issues, we propose Argus++, a robust real-time activitydetection system for analyzing unconstrained video streams. The design ofArgus++ introduces overlapping spatio-temporal cubes as an intermediate conceptof activity proposals to ensure coverage and completeness of activity detectionthrough over-sampling. The overall system is optimized for real-time processingon standalone consumer-level hardware. Extensive experiments on differentsurveillance and driving scenarios demonstrated its superior performance in aseries of activity detection benchmarks, including CVPR ActivityNet ActEV 2021,NIST ActEV SDL UF/KF, TRECVID ActEV 2020/2021, and ICCV ROAD 2021.

Argus++: Robust Real-time Activity Detection for Unconstrained Video Streams with Overlapping Cube Proposals