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Action recognition using ensemble weighted multi-instance learning

Chen, Guang; Giuliani, Manuel; Clarke, Daniel; Gaschler, Andre; Knoll, Alois

Authors

Guang Chen

Manuel Giuliani Manuel.Giuliani@uwe.ac.uk
Professor in Embedded Cognitive AI for Robotics

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.

Journal Article Type Conference Paper
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
APA6 Citation Chen, G., Giuliani, M., Clarke, D., Gaschler, A., & Knoll, A. (2014). Action recognition using ensemble weighted multi-instance learning. IEEE International Conference on Robotics and Automation, 4520-4525. https://doi.org/10.1109/ICRA.2014.6907519
DOI https://doi.org/10.1109/ICRA.2014.6907519
Keywords joints, three-dimensional displays, training, kernel, histograms, feature extraction
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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