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...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Book Section |
| Language: | English |
| Published: |
Springer
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/47325/ http://psasir.upm.edu.my/id/eprint/47325/1/Implementing%20low%20level%20features%20for%20human%20aggressive%20movement%20detection.pdf |
| _version_ | 1848850793765011456 |
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| author | Tan Zizi, Tuan Khalisah Ramli, Suzaimah Ibrahim, Norazlin Zainudin, Norulzahrah Abdullah, Lili Nurliyana Hasbullah, Nor Asiakin |
| 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 |
| author_sort | Tan Zizi, Tuan Khalisah |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T10:11:56Z |
| format | Book Section |
| id | upm-47325 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:11:56Z |
| publishDate | 2015 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| 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 |
| title | 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_short | Implementing low level features for human aggressive movement detection |
| title_sort | implementing low level features for human aggressive movement detection |
| url | http://psasir.upm.edu.my/id/eprint/47325/ http://psasir.upm.edu.my/id/eprint/47325/ http://psasir.upm.edu.my/id/eprint/47325/ http://psasir.upm.edu.my/id/eprint/47325/1/Implementing%20low%20level%20features%20for%20human%20aggressive%20movement%20detection.pdf |