Detection of Human Bodies in Lying Position based on Aggregate Channel Features
In recent years, detection of human body has drawn a lot of attention from researchers in the field of image recognition, with most work focused on pedestrian detection. The detection of human bodies in lying position also received numerous attention in applications such as elderly fall detection, s...
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Sajat M.A.S.; Hashim H.; Tahir N.M. |
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Sajat M.A.S.; Hashim H.; Tahir N.M. 2-s2.0-85084292922 Detection of Human Bodies in Lying Position based on Aggregate Channel Features 2020 Proceedings - 2020 16th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2020 10.1109/CSPA48992.2020.9068526 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084292922&doi=10.1109%2fCSPA48992.2020.9068526&partnerID=40&md5=db0a370977850ecd781457ae61c9abbb In recent years, detection of human body has drawn a lot of attention from researchers in the field of image recognition, with most work focused on pedestrian detection. The detection of human bodies in lying position also received numerous attention in applications such as elderly fall detection, sleep studies as well as in search and rescue (SAR) operations. Thus, in this paper, feature extraction performed by the Aggregate Channel Features (ACF) algorithm is explored for detection of human bodies in lying positions. ACF makes use of a Boosted Decision Tree (BDT) classifier that has resulted in increase in speed of detection. The classification was carried out using a dataset developed from aerial images of human bodies obtained from the internet. Initial result showed that the accuracy of ACF using the given dataset is 88% and the value of F-measure obtained was 0.9231. This proposed method will be further explored on a more advanced dataset. © 2020 IEEE. Institute of Electrical and Electronics Engineers Inc. English Conference paper |
| author |
2-s2.0-85084292922 |
| spellingShingle |
2-s2.0-85084292922 Detection of Human Bodies in Lying Position based on Aggregate Channel Features |
| author_facet |
2-s2.0-85084292922 |
| author_sort |
2-s2.0-85084292922 |
| title |
Detection of Human Bodies in Lying Position based on Aggregate Channel Features |
| title_short |
Detection of Human Bodies in Lying Position based on Aggregate Channel Features |
| title_full |
Detection of Human Bodies in Lying Position based on Aggregate Channel Features |
| title_fullStr |
Detection of Human Bodies in Lying Position based on Aggregate Channel Features |
| title_full_unstemmed |
Detection of Human Bodies in Lying Position based on Aggregate Channel Features |
| title_sort |
Detection of Human Bodies in Lying Position based on Aggregate Channel Features |
| publishDate |
2020 |
| container_title |
Proceedings - 2020 16th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2020 |
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| container_issue |
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| doi_str_mv |
10.1109/CSPA48992.2020.9068526 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084292922&doi=10.1109%2fCSPA48992.2020.9068526&partnerID=40&md5=db0a370977850ecd781457ae61c9abbb |
| description |
In recent years, detection of human body has drawn a lot of attention from researchers in the field of image recognition, with most work focused on pedestrian detection. The detection of human bodies in lying position also received numerous attention in applications such as elderly fall detection, sleep studies as well as in search and rescue (SAR) operations. Thus, in this paper, feature extraction performed by the Aggregate Channel Features (ACF) algorithm is explored for detection of human bodies in lying positions. ACF makes use of a Boosted Decision Tree (BDT) classifier that has resulted in increase in speed of detection. The classification was carried out using a dataset developed from aerial images of human bodies obtained from the internet. Initial result showed that the accuracy of ACF using the given dataset is 88% and the value of F-measure obtained was 0.9231. This proposed method will be further explored on a more advanced dataset. © 2020 IEEE. |
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Institute of Electrical and Electronics Engineers Inc. |
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| language |
English |
| format |
Conference paper |
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scopus |
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Scopus |
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1828987874025930752 |