Video based human activities recognition using deep learning
Human activities recognition from motion capture data is a challenging problem in the computer vision due to the fact that, in various human activities, different body components have distinctive characteristics in terms of the moving pattern. In this paper, a learning method of detecting an activi...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English English English |
| Published: |
AIP Publishing
2020
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/82391/18/82391_Video%20Based%20Human%20Activities%20Recognition%20using%20Deep_new.pdf http://irep.iium.edu.my/82391/7/Notification%20of%20Acceptance%20ICEDSA%202020%20%20%2327%20%20%20Video%20Based%20Human%20Activities%20Recognition%20Using%20Deep%20learning.pdf http://irep.iium.edu.my/82391/13/Certificate%20%20ICEDSA%202020%20%20%2327%20%20%20Video%20Based%20Human%20Activities%20Recognition%20Using%20Deep%20learning.pdf http://irep.iium.edu.my/82391/ https://aip.scitation.org/doi/10.1063/5.0032379 |
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| Summary: | Human activities recognition from motion capture data is a challenging problem in the computer vision due to the fact that, in various human activities, different body components have distinctive characteristics in terms of the moving pattern. In this paper, a learning method of detecting an activities from different angles based on various sources of information is proposed. with high accuracy. The bottomup approach is used in OpenPose which is the tool used in this paper’s experiments The proposed method achieve promising results on the MHAD datasets at 98% accuracy. |
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