Loading [a11y]/accessibility-menu.js
InfoGCN++: Learning Representation by Predicting the Future for Online Skeleton-Based Action Recognition | IEEE Journals & Magazine | IEEE Xplore

InfoGCN++: Learning Representation by Predicting the Future for Online Skeleton-Based Action Recognition


Abstract:

Skeleton-based action recognition has made significant advancements recently, with models like InfoGCN showcasing remarkable accuracy. However, these models exhibit a key...Show More

Abstract:

Skeleton-based action recognition has made significant advancements recently, with models like InfoGCN showcasing remarkable accuracy. However, these models exhibit a key limitation: they necessitate complete action observation prior to classification, which constrains their applicability in real-time situations such as surveillance and robotic systems. To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeleton-based action recognition. InfoGCN++ augments the abilities of the original InfoGCN model by allowing real-time categorization of action types, independent of the observation sequence’s length. It transcends conventional approaches by learning from current and anticipated future movements, thereby creating a more thorough representation of the entire sequence. Our approach to prediction is managed as an extrapolation issue, grounded on observed actions. To enable this, InfoGCN++ incorporates Neural Ordinary Differential Equations, a concept that lets it effectively model the continuous evolution of hidden states. Following rigorous evaluations on three skeleton-based action recognition benchmarks, InfoGCN++ demonstrates exceptional performance in online action recognition. It consistently equals or exceeds existing techniques, highlighting its significant potential to reshape the landscape of real-time action recognition applications. Consequently, this work represents a major leap forward from InfoGCN, pushing the limits of what’s possible in online, skeleton-based action recognition.
Page(s): 514 - 528
Date of Publication: 26 September 2024

ISSN Information:

PubMed ID: 39325606

Funding Agency:


Contact IEEE to Subscribe

References

References is not available for this document.