Garuda: a deep learning based solution for capturing selfies safely

Jitender Singh Virk, Abhinav Dhall

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

Abstract

Clicking selfies using mobile phones has become a trend in the past few years. It is documented that the thrill of clicking selfies at adventurous places has resulted in serious injuries and even death in some cases. To overcome this, we propose a system which can alert the user by detecting the level of danger in the background while capturing selfies. Our app is based on a deep Convolutional Neural Network (CNN). The prediction is performed as a 5 class classification problem with classes representing a different level of danger. Face detection and device orientation information are also used for robustness and lesser battery consumption.

Original languageEnglish
Title of host publicationProceedings of IUI 2019
EditorsOliver Brdiczka, Gaëlle Calvary, Polo Chau
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages43-44
Number of pages2
ISBN (Electronic)9781450366731
DOIs
Publication statusPublished - 16 Mar 2019
Externally publishedYes
EventInternational Conference on Intelligent User Interfaces 2019 - Los Angeles, United States of America
Duration: 16 Mar 201920 Mar 2019
Conference number: 24th
http://iui.acm.org/2019/

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI

Conference

ConferenceInternational Conference on Intelligent User Interfaces 2019
Abbreviated titleIUI 2019
CountryUnited States of America
CityLos Angeles
Period16/03/1920/03/19
Internet address

Keywords

  • Deep learning
  • Safe selfie
  • Scene analysis
  • Selfie

Cite this

Singh Virk, J., & Dhall, A. (2019). Garuda: a deep learning based solution for capturing selfies safely. In O. Brdiczka, G. Calvary, & P. Chau (Eds.), Proceedings of IUI 2019 (pp. 43-44). (International Conference on Intelligent User Interfaces, Proceedings IUI). Association for Computing Machinery (ACM). https://doi.org/10.1145/3308557.3308669, https://doi.org/10.1145/3308557.3308669