Straightforward Recognition of Daily Objects in Smart Environments from Wearable Vision Sensor

Javier Medina Quero, Federico Cruciani, Lorenzo Seidenari, Macarena Espinilla, CD Nugent

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

Abstract

In this work, we propose a method to create and
synthesize a new set of virtual images of daily objects within
a smart environment partially automating the labeling process.
Proposed methods enable the generation of a large dataset
from a set of few images using an ad hoc data augmentation,
which increases the original dataset size, generating new items
through partial modification of available images. The proposed
method for data augmentation is accomplished through the
following steps: (i) object tracking is proposed to identify and
label static objects; and (ii) background subtraction is used to
select the masked foreground object of moving objects, which
are virtually projected with geometry transformation over room
images used as background. Furthermore, a case study is carried
out, where an inhabitant wears a wearable vision sensor in a daily
scene. Eight objects are learned using the proposed methodology.
Finally, obtained results and successful recognition rates are
discussed.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019
PublisherIEEE
Pages46-51
Number of pages6
ISBN (Electronic)9781538691519
DOIs
Publication statusPublished - 6 Jun 2019
Event2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019: PerCom Pervasive Computing 2019 - Kyoto, Japan
Duration: 11 Mar 201915 Mar 2019
http://www.percom.org/Previous/ST2019/home.html

Conference

Conference2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019
Abbreviated titlePerCom 2019
CountryJapan
CityKyoto
Period11/03/1915/03/19
Internet address

Keywords

  • activity recognition
  • data augmentation
  • deep learning
  • object recognition
  • smart environments
  • wearable vision sensors

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