A Hierarchical Human Activity Recognition Framework Based on Automated Reasoning

Shuwei Chen, Jun Liu, Hui Wang, Juan Carlos Augusto

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

10 Citations (Scopus)

Abstract

Conventional human activity recognition approaches are mainly based on machine learning methods, which are not working well for composite activity recognition due to the complexity and uncertainty of real scenarios. We propose in this paper an automated reasoning based hierarchical framework for human activity recognition. This approach constructs a hierarchical structure for representing the composite activity by a composition of lower-level actions and gestures according to its semantic meaning. This hierarchical structure is then transformed into logical formulas and rules, based on which the resolution based automated reasoning is applied to recognize the composite activity given the recognized lower-level actions by machine learning methods.
Original languageEnglish
Title of host publication2013 IEEE International Conference on Systems, Man, and Cybernetics: Proceedings
Place of PublicationUnited States
Publisher IEEE
Pages3495-3499
Number of pages5
ISBN (Electronic)978-1-4799-0652-9
DOIs
Publication statusPublished - 27 Jan 2014
Externally publishedYes

Publication series

NameIEEE International Conference on Systems, Man, and Cybernetics: Proceedings
PublisherIEEE
ISSN (Print)1062-922X

Bibliographical note

2013 IEEE International Conference on Systems, Man, and Cybernetics (IEEESMC2013) ; Conference date: 13-10-2013

Fingerprint

Dive into the research topics of 'A Hierarchical Human Activity Recognition Framework Based on Automated Reasoning'. Together they form a unique fingerprint.

Cite this