Human posture recognition results using database A
Human Posture Recognition gives machines the ability to detect, track and identify people and their actions from video, has become a central topic in computer vision research. Recognition of human posture is a very challenging problem. The training and evaluation stage, datasets of pre-processed po...
Saved in:
Main Authors: | , , |
---|---|
Format: | Book Chapter |
Language: | English |
Published: |
IIUM Press
2011
|
Subjects: | |
Online Access: | http://irep.iium.edu.my/21631/1/Chapter_7.pdf http://irep.iium.edu.my/21631/ http://rms.research.iium.edu.my/bookstore/default.aspx |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.iium.irep.21631 |
---|---|
record_format |
dspace |
spelling |
my.iium.irep.216312020-10-30T00:12:57Z http://irep.iium.edu.my/21631/ Human posture recognition results using database A Htike, Kyaw Kyaw Khalifa, Othman Omran Lai, Weng Kin TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Human Posture Recognition gives machines the ability to detect, track and identify people and their actions from video, has become a central topic in computer vision research. Recognition of human posture is a very challenging problem. The training and evaluation stage, datasets of pre-processed posture images are needed. After the video sequences have been prepared, for the training and evaluation stage, they are pre-processed to produce three different types of datasets. This chapter will explain the results obtained using Dataset A. This dataset was obtained after pre-processing some of the video sequences that were taken at MIMOS building. In this dataset, there are six types of postures considered. The "unknown" posture was added so that during the training stage, the system could learn to differentiate postures which cannot be classified as belonging to any of the other types. This dataset is the main dataset for evaluating and comparing the performance (in terms of accuracy, i.e. recognition rate) of the different classifiers IIUM Press 2011 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/21631/1/Chapter_7.pdf Htike, Kyaw Kyaw and Khalifa, Othman Omran and Lai, Weng Kin (2011) Human posture recognition results using database A. In: Human Behaviour Recognition, Identification and Computer Interaction. IIUM Press, Kuala Lumpur, pp. 49-57. ISBN 978-967-418-156-7 http://rms.research.iium.edu.my/bookstore/default.aspx |
institution |
Universiti Islam Antarabangsa Malaysia |
building |
IIUM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
International Islamic University Malaysia |
content_source |
IIUM Repository (IREP) |
url_provider |
http://irep.iium.edu.my/ |
language |
English |
topic |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
spellingShingle |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Htike, Kyaw Kyaw Khalifa, Othman Omran Lai, Weng Kin Human posture recognition results using database A |
description |
Human Posture Recognition gives machines the ability to detect, track and identify people and their actions from video, has become a central topic in computer vision research. Recognition of human posture is a very challenging problem. The training and evaluation
stage, datasets of pre-processed posture images are needed. After the video sequences have been prepared, for the training and evaluation stage, they are pre-processed to produce three different types of datasets. This chapter will explain the results obtained using Dataset A.
This dataset was obtained after pre-processing some of the video sequences that were taken at MIMOS building. In this dataset, there are six types of postures considered. The "unknown" posture was added so that during the training stage, the system could learn to differentiate
postures which cannot be classified as belonging to any of the other types. This dataset is the main dataset for evaluating and comparing the performance (in terms of accuracy, i.e. recognition rate) of the different classifiers |
format |
Book Chapter |
author |
Htike, Kyaw Kyaw Khalifa, Othman Omran Lai, Weng Kin |
author_facet |
Htike, Kyaw Kyaw Khalifa, Othman Omran Lai, Weng Kin |
author_sort |
Htike, Kyaw Kyaw |
title |
Human posture recognition results using database A |
title_short |
Human posture recognition results using database A |
title_full |
Human posture recognition results using database A |
title_fullStr |
Human posture recognition results using database A |
title_full_unstemmed |
Human posture recognition results using database A |
title_sort |
human posture recognition results using database a |
publisher |
IIUM Press |
publishDate |
2011 |
url |
http://irep.iium.edu.my/21631/1/Chapter_7.pdf http://irep.iium.edu.my/21631/ http://rms.research.iium.edu.my/bookstore/default.aspx |
_version_ |
1683230265200082944 |
score |
13.211869 |