Robust spatio-Temporal features for human interaction recognition via artificial neural network

Maria Mahmood, Ahmad Jalal, M. A. Sidduqi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

90 Scopus citations

Abstract

Human Interaction Recognition plays a key role in identification of usual and unusual human behaviors and facilitates public dealings, violence detection, robots perception, and virtual entertainments. This paper presents a novel human interaction recognition (HIR) system to recognize human interactions in continuous image sequences. The proposed technology segments full body silhouettes and identifies key body points to extract robust spatio-Temporal features having distinct characteristics for each interaction. Our descriptors focus on local descriptions, capture intensity variations, point-To-point distances and time based relations. The system is trained through artificial neural network to recognize six basic interactions taken from UT-Interaction dataset.
Original languageEnglish (US)
Title of host publication2018 International Conference on Frontiers of Information Technology (FIT)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages218-223
Number of pages6
ISBN (Print)9781538693551
DOIs
StatePublished - Jan 18 2019
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research is supported by the Engineering and Managing information Centers, Saudi Arabia, under the “NVorio 5.5 Software program” (Access No. AFRT-2-04-827502) cooperated with the SNCIS (Saudi National Centre for Innovation Science).

Fingerprint

Dive into the research topics of 'Robust spatio-Temporal features for human interaction recognition via artificial neural network'. Together they form a unique fingerprint.

Cite this