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Mutually reinforcing motion-pose framework for pose invariant action recognition
- Source :
- International Journal of Biometrics; 2019, Vol. 11 Issue: 2 p113-147, 35p
- Publication Year :
- 2019
-
Abstract
- Action recognition from videos has many potential applications. However, there are many unresolved challenges, such as pose-invariant recognition, robustness to occlusion and others. In this paper, we propose to combine motion of body parts and pose hypothesis generation validated with specific canonical poses observed in a novel mutually reinforcing framework to achieve pose-invariant action recognition. To capture the temporal dynamics of an action, we introduce temporal stick features computed using the stick poses obtained. The combination of pose-invariant kinematic features from motion, pose hypothesis and temporal stick features are used for action recognition, thus forming a mutually reinforcing framework that repeats until the action recognition result converges. The proposed mutual reinforcement framework is capable of handling changes in posture of the person, occlusion and partial view-invariance. We perform experiments on several benchmark datasets which showed the performance of the proposed algorithm and its ability to handle pose variation and occlusion.
Details
- Language :
- English
- ISSN :
- 17558301 and 1755831X
- Volume :
- 11
- Issue :
- 2
- Database :
- Supplemental Index
- Journal :
- International Journal of Biometrics
- Publication Type :
- Periodical
- Accession number :
- ejs49787178
- Full Text :
- https://doi.org/10.1504/IJBM.2019.099014