Activity recognition using conditional random field

Megha Agarwal, Peter Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)

Abstract

Activity Recognition is an integral component of ubiquitous computing. Recognizing an activity is a challenging task since activities can be concurrent, interleaved or ambiguous and can consist of multiple actors (which would require parallel activity recognition). This paper investigates how the discriminative nature of Conditional Random Fields (CRF) can be exploited to enhance the accuracy of recognizing activities when compared to that achieved using generative models. It aims to apply CRF to recognize complex activities, analyze the model trained by CRF and evaluate the performance of CRF against existing models using Stochastic Gradient Descent (which is suitable for online learning).

Original languageEnglish
Title of host publicationProceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction
DOIs
Publication statusPublished - 25 Jun 2015
Event2nd International Workshop on Sensor-Based Activity Recognition and Interaction, iWOAR 2015 - Rostock, Germany
Duration: 25 Jun 201526 Jun 2015

Conference

Conference2nd International Workshop on Sensor-Based Activity Recognition and Interaction, iWOAR 2015
CountryGermany
CityRostock
Period25/06/1526/06/15

Structured keywords

  • Jean Golding

Keywords

  • Activities of daily living
  • Conditional Random Fields
  • Online learning
  • Sensor based activity recognition
  • Supervised learning

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