Guang Chen
Action recognition using ensemble weighted multi-instance learning
Chen, Guang; Giuliani, Manuel; Clarke, Daniel; Gaschler, Andre; Knoll, Alois
Authors
Manuel Giuliani Manuel.Giuliani@uwe.ac.uk
Co- Director Bristol Robotics Laboratory
Daniel Clarke
Andre Gaschler
Alois Knoll
Abstract
© 2014 IEEE. This paper deals with recognizing human actions in depth video data. Current state-of-the-art action recognition methods use hand-designed features, which are difficult to produce and time-consuming to extend to new modalities. In this paper, we propose a novel, 3.5D representation of a depth video for action recognition. A 3.5D graph of the depth video consists of a set of nodes that are the joints of the human body. Each joint is represented by a set of spatio-temporal features, which are computed by an unsupervised learning approach. However, if occlusions occur, the 3D positions of the joints are noisy which increases the intra-class variations in action classes. To address this problem, we propose the Ensemble Weighted Multi-Instance Learning approach (EnwMi) for the action recognition task. It considers the class imbalance and intra-class variations. We formulate the action recognition task with depth videos as a weighted multi-instance problem. We further integrate an ensemble learning method into the weighted multi-instance learning framework. Our approach is evaluated on Microsoft Research Action3D dataset, and the results show that it outperforms state-of-the-art methods.
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | Proceedings of the IEEE International Conference on Robotics and Automation 2014 (ICRA 2014) |
Acceptance Date | May 31, 2014 |
Publication Date | Jan 1, 2014 |
Journal | Proceedings - IEEE International Conference on Robotics and Automation |
Print ISSN | 1050-4729 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Pages | 4520-4525 |
DOI | https://doi.org/10.1109/ICRA.2014.6907519 |
Keywords | joints, three-dimensional displays, training, kernel, histograms, feature extraction |
Public URL | https://uwe-repository.worktribe.com/output/811624 |
Publisher URL | http://dx.doi.org/10.1109/ICRA.2014.6907519 |
Additional Information | Title of Conference or Conference Proceedings : 2014 IEEE International Conference on Robotics and Automation (ICRA) |
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