Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142072
Title: Mutually reinforcing motion-pose framework for pose invariant action recognition
Authors: Ramanathan, Manoj
Yau, Wei-Yun
Thalmann, Nadia Magnenat
Teoh, Eam Khwang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Ramanathan, M., Yau, W.-Y., Thalmann, N. M., & Teoh, E. W. (2019). Mutually reinforcing motion-pose framework for pose invariant action recognition. International Journal of Biometrics, 11(2), 113-147. doi:10.1504/IJBM.2019.099014
Journal: International Journal of Biometrics
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.
URI: https://hdl.handle.net/10356/142072
ISSN: 1755-8301
DOI: 10.1504/IJBM.2019.099014
Rights: © 2019 Inderscience Enterprises Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:IMI Journal Articles

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