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

Real-time eyeblink detection in the wild can widely serve for fatiguedetection, face anti-spoofing, emotion analysis, etc. The existing researchefforts generally focus on single-person cases towards trimmed video. However,multi-person scenario within untrimmed videos is also important for practicalapplications, which has not been well concerned yet. To address this, we shedlight on this research field for the first time with essential contributions ondataset, theory, and practices. In particular, a large-scale dataset termedMPEblink that involves 686 untrimmed videos with 8748 eyeblink events isproposed under multi-person conditions. The samples are captured fromunconstrained films to reveal "in the wild" characteristics. Meanwhile, areal-time multi-person eyeblink detection method is also proposed. Beingdifferent from the existing counterparts, our proposition runs in a one-stagespatio-temporal way with end-to-end learning capacity. Specifically, itsimultaneously addresses the sub-tasks of face detection, face tracking, andhuman instance-level eyeblink detection. This paradigm holds 2 main advantages:(1) eyeblink features can be facilitated via the face's global context (e.g.,head pose and illumination condition) with joint optimization and interaction,and (2) addressing these sub-tasks in parallel instead of sequential manner cansave time remarkably to meet the real-time running requirement. Experiments onMPEblink verify the essential challenges of real-time multi-person eyeblinkdetection in the wild for untrimmed video. Our method also outperforms existingapproaches by large margins and with a high inference speed.

Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video