Multimodal sequential modeling and recognition of human activities

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

Abstract

Video-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support independent living of old people. In this work, we propose a new multimodal ADL recognition method by modeling the correlation between motion and object information. We encode motion using dense interest point trajectories which are robust to occlusion and speed variability. We formulate the learning problem using a two-layer SVM hidden conditional random field (HCRF) recognition model that is particularly relevant for multimodal sequence recognition. This hierarchical classifier optimally combines the discriminative power of SVM and the long-range feature dependencies modeling by the HCRF.

Original languageEnglish
Title of host publicationComputers Helping People with Special Needs - 15th International Conference, ICCHP 2016, Proceedings
EditorsChristian Bühler, Petr Penaz, Klaus Miesenberger
PublisherSpringer Verlag
Pages541-548
Number of pages8
ISBN (Print)9783319412665
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event15th International Conference on Computers Helping People with Special Needs, ICCHP 2016 - Linz, Austria
Duration: 13 Jul 201615 Jul 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9759
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Computers Helping People with Special Needs, ICCHP 2016
Country/TerritoryAustria
CityLinz
Period13/07/1615/07/16

Keywords

  • Activities of daily living
  • Ambient assisted living system
  • Interest points
  • Multimodal representation
  • SVM-HCRF

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