TY - GEN
T1 - Human skeleton feature extraction from 2-dimensional video of Indonesian language sign system (SIBI [Sistem Isyarat Bahasa Indonesia]) gestures
AU - Pratama, Aulia Astrico
AU - Rakun, Erdefi
AU - Hardianto, Dadan
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/4/19
Y1 - 2019/4/19
N2 - 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%.
AB - 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%.
KW - Computer vision
KW - Feature extraction
KW - Gesture recognition
KW - Image processing
KW - Skeleton extraction
UR - http://www.scopus.com/inward/record.url?scp=85071123574&partnerID=8YFLogxK
U2 - 10.1145/3330482.3330484
DO - 10.1145/3330482.3330484
M3 - Conference contribution
AN - SCOPUS:85071123574
T3 - ACM International Conference Proceeding Series
SP - 100
EP - 105
BT - ICCAI 2019 - 2019 5th International Conference on Computing and Artificial Intelligence
PB - Association for Computing Machinery
T2 - 5th International Conference on Computing and Artificial Intelligence, ICCAI 2019
Y2 - 19 April 2019 through 22 April 2019
ER -