Recaspia: Recognizing Carrying Actions in Single Images Using Privileged Information

Christos Smailis, Michalis Vrigkas, Ioannis A. Kakadiaris

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

4 Scopus citations

Abstract

Many approaches for action recognition focus on general actions, such as 'running' or 'walking'. This work presents a method for recognizing carrying actions in single images, by utilizing privileged information, such as annotation, available only during training, following the learning using privileged information paradigm. In addition, we introduce a dataset for carrying actions, formed using images extracted from YouTube videos depicting several scenarios. We accompany the dataset with a variety of different annotation types that include human pose, object and scene attributes. The experimental results demonstrate that our method, boosted sample averaged F1 score performance by 15.4% and 4.15%, respectively, in the validation and testing partitions of our dataset, when compared to an end-to-end CNN model, trained only with the observable information.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages26-30
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: Sep 22 2019Sep 25 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan, Province of China
CityTaipei
Period9/22/199/25/19

Keywords

  • Action Recognition
  • Deep Learning
  • LUPI
  • Privileged Information
  • Static Images

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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