Human skeleton feature extraction from 2-dimensional video of Indonesian language sign system (SIBI [Sistem Isyarat Bahasa Indonesia]) gestures

Aulia Astrico Pratama, Erdefi Rakun, Dadan Hardianto

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

2 Citations (Scopus)

Abstract

Indonesian Language Sign System (SIBI) is the official sign language system used in Indonesia. A model that could translate SIBI gesture taken from a video would be very useful for communicating with people with disabilities. One of the features needed to translate SIBI gesture to words is the subject’s skeleton. In this paper, we researched a method to extract this feature from 2-Dimensional video. The method reconstructs skeleton model based on the position of head, shoulders, elbows, and hands of the subject. The head is located with haar cascade and the shoulders are pinpointed based on the location of the head. The hands are located with skin segmentation technique and then tracked throughout the video with Lucas-Kanade method. The elbows are extrapolated based on the shoulder and hand points, and the body silhouette. The experiment with LSTM model resulted in maximum testing accuracy of 98.214%.

Original languageEnglish
Title of host publicationICCAI 2019 - 2019 5th International Conference on Computing and Artificial Intelligence
PublisherAssociation for Computing Machinery
Pages100-105
Number of pages6
ISBN (Electronic)9781450361064
DOIs
Publication statusPublished - 19 Apr 2019
Event5th International Conference on Computing and Artificial Intelligence, ICCAI 2019 - Bali, Indonesia
Duration: 19 Apr 201922 Apr 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Computing and Artificial Intelligence, ICCAI 2019
CountryIndonesia
CityBali
Period19/04/1922/04/19

Keywords

  • Computer vision
  • Feature extraction
  • Gesture recognition
  • Image processing
  • Skeleton extraction

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