Human upper body pose region estimation

The objective of this chapter is to estimate 2D human pose for action recognition and especially for sign language recognition systems which require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB part...

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Main Authors:	Bilal, Sara Mohammed Osman Saleh, Akmeliawati, Rini, Shafie, Amir Akramin, Salami, Momoh Jimoh Eyiomika
Format:	Book Chapter
Language:	English
Published:	Springer 2013
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/31777/ http://irep.iium.edu.my/31777/ http://irep.iium.edu.my/31777/1/Book_RINI.pdf

id	iium-31777
recordtype	eprints
spelling	iium-317772014-03-08T19:57:54Z http://irep.iium.edu.my/31777/ Human upper body pose region estimation Bilal, Sara Mohammed Osman Saleh Akmeliawati, Rini Shafie, Amir Akramin Salami, Momoh Jimoh Eyiomika TK Electrical engineering. Electronics Nuclear engineering The objective of this chapter is to estimate 2D human pose for action recognition and especially for sign language recognition systems which require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. We propose an approach that progressively reduces the search space for body parts and can greatly improve chance to estimate the HUB pose. This involves two contributions: (a) a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. (b) Scaling the extracted parts during body orientation was attained using partial estimation of face size. The outcome of the system makes it applicable for real-time applications such as sign languages recognition systems. The method is fully automatic and self-initializing using a Haar-like face region. The tracking the HUB pose is based on the face detection algorithm. Our evaluation was done mainly using 50 images from INRIA Person Dataset. Springer 2013 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/31777/1/Book_RINI.pdf Bilal, Sara Mohammed Osman Saleh and Akmeliawati, Rini and Shafie, Amir Akramin and Salami, Momoh Jimoh Eyiomika (2013) Human upper body pose region estimation. In: Recent advances in robotics and automation, studies in computational intelligence. Studies in computational intelligence (480). Springer, London, pp. 335-344. ISBN 9783642373862 / 9783642373879 http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-37386-2
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Bilal, Sara Mohammed Osman Saleh Akmeliawati, Rini Shafie, Amir Akramin Salami, Momoh Jimoh Eyiomika Human upper body pose region estimation
description	The objective of this chapter is to estimate 2D human pose for action recognition and especially for sign language recognition systems which require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. We propose an approach that progressively reduces the search space for body parts and can greatly improve chance to estimate the HUB pose. This involves two contributions: (a) a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. (b) Scaling the extracted parts during body orientation was attained using partial estimation of face size. The outcome of the system makes it applicable for real-time applications such as sign languages recognition systems. The method is fully automatic and self-initializing using a Haar-like face region. The tracking the HUB pose is based on the face detection algorithm. Our evaluation was done mainly using 50 images from INRIA Person Dataset.
format	Book Chapter
author	Bilal, Sara Mohammed Osman Saleh Akmeliawati, Rini Shafie, Amir Akramin Salami, Momoh Jimoh Eyiomika
author_facet	Bilal, Sara Mohammed Osman Saleh Akmeliawati, Rini Shafie, Amir Akramin Salami, Momoh Jimoh Eyiomika
author_sort	Bilal, Sara Mohammed Osman Saleh
title	Human upper body pose region estimation
title_short	Human upper body pose region estimation
title_full	Human upper body pose region estimation
title_fullStr	Human upper body pose region estimation
title_full_unstemmed	Human upper body pose region estimation
title_sort	human upper body pose region estimation
publisher	Springer
publishDate	2013
url	http://irep.iium.edu.my/31777/ http://irep.iium.edu.my/31777/ http://irep.iium.edu.my/31777/1/Book_RINI.pdf
first_indexed	2023-09-18T20:45:55Z
last_indexed	2023-09-18T20:45:55Z
_version_	1777409674133372928

Human upper body pose region estimation

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