Mendeley
TY  _  JOUR
ID  -  2021050316060
TI  -  Rough Sets Theory with Deep Learning for Tracking in Natural Interaction with Deaf
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Ebrahimi Mohammad
AU  -  Ebrhimpour-Komeleh Hossein
AD  -  دانشجوی دکترای دانشکده برق و کامپیوتر دانشگاه کاشان
AD  -  
Y1  -  2022
PY  -  2022
VL  -  39
IS  -  
SP  -  211
EP  -  221
KW  -   Natural Interaction with Deaf
KW  -   Machine Vision
KW  -   Persian Deaf News Hand Tracking
KW  -   Sign Language
KW  -   Rough Sets Theory
KW  -   Deep Learning.
DO  -  
N2  -  Sign languages commonly serve as an alternative or complementary mode of human communication Tracking is one of the most fundamental problems in computer vision, and use in a long list of applications such as sign languages recognition. Despite great advances in recent years, tracking remains challenging due to many factors including occlusion, scale variation, etc. The mistake detecting of head or left hand instead of right hand in overlapping are, modes like this, and due to the uncertainty of the hand area over the deaf news video frames; we proposed two methods: first, tracking using particle filter and second tracking using the idea of the rough set theory in granular information with deep neural network. We proposed the method for Combination the Rough Set with Deep Neural Network and used for in Hand/Head Tracking in Video Signal DeafNews. We develop a tracking system for Deaf News. We used rough set theory to increase the accuracy of skin segmentation in video signal. Using deep neural network, we extracted inherent relationships available in the frame pixels and generalized the achieved features to tracking. The system proposed is tested on the 33 of Deaf News with 100 different words and 1927 video files for words then recall, MOTA and MOTP values are obtained.
UR  - rimag.ir/en/Article/16060
L1  - rimag.ir/en/Article/Download/16060
ER  -  
