Human activity recognition in low quality videos using spatio-temporal features
Human activity recognition (HAR) is one of the most intensively studied areas of computer vision in recent times. However, under real world conditions, especially when public infrastructure such as surveillance and web cameras are considered, current HAR techniques do not adapt to lower quality vide...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 出版: |
2016
|
| 主题: | |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
| id |
my-mmu-ep.12830 |
|---|---|
| record_format |
uketd_dc |
| spelling |
my-mmu-ep.128302024-08-16T03:34:40Z Human activity recognition in low quality videos using spatio-temporal features 2016-06 Rahman, Saimunur TK7800-8360 Electronics Human activity recognition (HAR) is one of the most intensively studied areas of computer vision in recent times. However, under real world conditions, especially when public infrastructure such as surveillance and web cameras are considered, current HAR techniques do not adapt to lower quality videos due to various challenges such as noise and lighting changes, motion blur, poor resolution and sampling. The objective of this research is to develop a framework and methods for human activity recognition using spatio-temporal information from low quality video. Overall, it can be observed that texture is an important visual feature cue for low quality video, and the robustness of shape and motion feature can be strengthened by using this. 2016-06 Thesis https://shdl.mmu.edu.my/12830/ http://erep.mmu.edu.my/ masters Multimedia University Faculty of Computing and Informatics (FCI) EREP ID: 12257 |
| institution |
Multimedia University |
| collection |
MMU Institutional Repository |
| topic |
TK7800-8360 Electronics |
| spellingShingle |
TK7800-8360 Electronics Rahman, Saimunur Human activity recognition in low quality videos using spatio-temporal features |
| description |
Human activity recognition (HAR) is one of the most intensively studied areas of computer vision in recent times. However, under real world conditions, especially when public infrastructure such as surveillance and web cameras are considered, current HAR techniques do not adapt to lower quality videos due to various challenges such as noise and lighting changes, motion blur, poor resolution and sampling. The objective of this research is to develop a framework and methods for human activity recognition using spatio-temporal information from low quality video. Overall, it can be observed that texture is an important visual feature cue for low quality video, and the robustness of shape and motion feature can be strengthened by using this. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Rahman, Saimunur |
| author_facet |
Rahman, Saimunur |
| author_sort |
Rahman, Saimunur |
| title |
Human activity recognition in low quality videos using spatio-temporal features |
| title_short |
Human activity recognition in low quality videos using spatio-temporal features |
| title_full |
Human activity recognition in low quality videos using spatio-temporal features |
| title_fullStr |
Human activity recognition in low quality videos using spatio-temporal features |
| title_full_unstemmed |
Human activity recognition in low quality videos using spatio-temporal features |
| title_sort |
human activity recognition in low quality videos using spatio-temporal features |
| granting_institution |
Multimedia University |
| granting_department |
Faculty of Computing and Informatics (FCI) |
| publishDate |
2016 |
| _version_ |
1811768004748247040 |