Action recognition using local visual descriptors and inertial data

Taha Alhersh*, Samir Brahim Belhaouari, Heiner Stuckenschmidt

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Different body sensors and modalities can be used in human action recognition, either separately or simultaneously. Multi-modal data can be used in recognizing human action. In this work we are using inertial measurement units (IMUs) positioned at left and right hands with first person vision for human action recognition. A novel statistical feature extraction method was proposed based on curvature of the graph of a function and tracking left and right hand positions in space. Local visual descriptors have been used as features for egocentric vision. An intermediate fusion between IMUs and visual sensors has been performed. Despite of using only two IMUs sensors with egocentric vision, our classification result achieved is 99.61% for recognizing nine different actions. Feature extraction step could play a vital step in human action recognition with limited number of sensors, hence, our method might indeed be promising.

Original languageEnglish
Title of host publicationAmbient Intelligence - 15th European Conference, AmI 2019, Proceedings
EditorsIoannis Chatzigiannakis, Boris De Ruyter, Irene Mavrommati
PublisherSpringer
Pages123-138
Number of pages16
ISBN (Print)9783030342548
DOIs
Publication statusPublished - 2019
Event15th European Conference on Ambient Intelligence, AmI 2019 - Rome, Italy
Duration: 13 Nov 201915 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11912 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th European Conference on Ambient Intelligence, AmI 2019
Country/TerritoryItaly
CityRome
Period13/11/1915/11/19

Keywords

  • Classification
  • Feature extraction
  • Human action recognition
  • IMUs
  • Sensor fusing
  • Visual descriptors

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