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Implementing low level features for human aggressive movement detection

In this real world, being able to identify the signs of imminent abnormal behaviors such as aggression or violence and also fights, is of extreme importance in keeping safe those in harm’s way. This research propose an approach to figure out human aggressive movements using Horn-Schunck optical flow...

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Main Authors: Tan Zizi, Tuan Khalisah, Ramli, Suzaimah, Ibrahim, Norazlin, Zainudin, Norulzahrah, Abdullah, Lili Nurliyana, Hasbullah, Nor Asiakin
Other Authors: Badioze Zaman, Halimah
Format: Book Section
Language:English
Published: Springer 2015
Online Access:http://psasir.upm.edu.my/id/eprint/47325/1/Implementing%20low%20level%20features%20for%20human%20aggressive%20movement%20detection.pdf
http://psasir.upm.edu.my/id/eprint/47325/
https://link.springer.com/chapter/10.1007/978-3-319-25939-0_26
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spelling my.upm.eprints.473252021-09-04T21:39:22Z http://psasir.upm.edu.my/id/eprint/47325/ Implementing low level features for human aggressive movement detection Tan Zizi, Tuan Khalisah Ramli, Suzaimah Ibrahim, Norazlin Zainudin, Norulzahrah Abdullah, Lili Nurliyana Hasbullah, Nor Asiakin In this real world, being able to identify the signs of imminent abnormal behaviors such as aggression or violence and also fights, is of extreme importance in keeping safe those in harm’s way. This research propose an approach to figure out human aggressive movements using Horn-Schunck optical flow algorithm in order to find the flow vector for all video frames. The video frames are collected using digital camera. This research guides and discovers the patterns of body distracted movement so that suspect of aggression can be investigated without body contact. Using the vector of this method, the abnormal and normal video frames are then classified and utilized to define the aggressiveness of humans. Preliminary experiment result showed that the low level of feature extraction can classify human aggressive and non-aggressive movements. Springer Badioze Zaman, Halimah Robinson, Peter Smeaton, Alan F. Shih, Timothy K. Velastin, Sergio Jaafar, Azizah Mohamad Ali, Nazlena 2015 Book Section PeerReviewed text en http://psasir.upm.edu.my/id/eprint/47325/1/Implementing%20low%20level%20features%20for%20human%20aggressive%20movement%20detection.pdf Tan Zizi, Tuan Khalisah and Ramli, Suzaimah and Ibrahim, Norazlin and Zainudin, Norulzahrah and Abdullah, Lili Nurliyana and Hasbullah, Nor Asiakin (2015) Implementing low level features for human aggressive movement detection. In: Advances in Visual Informatics. Lecture Notes in Computer Science . Springer, Switzerland, pp. 296-302. ISBN 9783319259383; EISBN: 9783319259390 https://link.springer.com/chapter/10.1007/978-3-319-25939-0_26 10.1007/978-3-319-25939-0_26
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description In this real world, being able to identify the signs of imminent abnormal behaviors such as aggression or violence and also fights, is of extreme importance in keeping safe those in harm’s way. This research propose an approach to figure out human aggressive movements using Horn-Schunck optical flow algorithm in order to find the flow vector for all video frames. The video frames are collected using digital camera. This research guides and discovers the patterns of body distracted movement so that suspect of aggression can be investigated without body contact. Using the vector of this method, the abnormal and normal video frames are then classified and utilized to define the aggressiveness of humans. Preliminary experiment result showed that the low level of feature extraction can classify human aggressive and non-aggressive movements.
author2 Badioze Zaman, Halimah
author_facet Badioze Zaman, Halimah
Tan Zizi, Tuan Khalisah
Ramli, Suzaimah
Ibrahim, Norazlin
Zainudin, Norulzahrah
Abdullah, Lili Nurliyana
Hasbullah, Nor Asiakin
format Book Section
author Tan Zizi, Tuan Khalisah
Ramli, Suzaimah
Ibrahim, Norazlin
Zainudin, Norulzahrah
Abdullah, Lili Nurliyana
Hasbullah, Nor Asiakin
spellingShingle Tan Zizi, Tuan Khalisah
Ramli, Suzaimah
Ibrahim, Norazlin
Zainudin, Norulzahrah
Abdullah, Lili Nurliyana
Hasbullah, Nor Asiakin
Implementing low level features for human aggressive movement detection
author_sort Tan Zizi, Tuan Khalisah
title Implementing low level features for human aggressive movement detection
title_short Implementing low level features for human aggressive movement detection
title_full Implementing low level features for human aggressive movement detection
title_fullStr Implementing low level features for human aggressive movement detection
title_full_unstemmed Implementing low level features for human aggressive movement detection
title_sort implementing low level features for human aggressive movement detection
publisher Springer
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/47325/1/Implementing%20low%20level%20features%20for%20human%20aggressive%20movement%20detection.pdf
http://psasir.upm.edu.my/id/eprint/47325/
https://link.springer.com/chapter/10.1007/978-3-319-25939-0_26
_version_ 1710677131688673280
score 13.211869

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