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

32 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

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