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Action recognition using ensemble weighted multi-instance learning

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


Guang Chen

Manuel Giuliani
Co- Director Bristol Robotics Laboratory

Daniel Clarke

Andre Gaschler

Alois Knoll


© 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
Keywords joints, three-dimensional displays, training, kernel, histograms, feature extraction
Public URL
Publisher URL
Additional Information Title of Conference or Conference Proceedings : 2014 IEEE International Conference on Robotics and Automation (ICRA)